What makes Toptal AI different?

We help organizations move AI from ambition to validated business outcomes. We combine specialized AI talent, delivery teams, proprietary capabilities, and leading technology partners to build around your data, workflows, systems, and business goals, then continuously evaluate, operate, and improve what gets deployed.

What AI services does Toptal provide?

Toptal AI helps organizations understand how work gets done, capture the activity and knowledge behind it, and turn that context into production AI systems. We combine workflow and process mapping, data and knowledge preparation, agent and model orchestration, evaluation, governance, expert-in-the-loop operations, and managed services to continuously improve performance against validated business outcomes.

Can you support an AI initiative from strategy through production?

Yes. We can support a focused initiative or take ownership across the full life cycle, from identifying the opportunity and designing the solution through building, integration, deployment, adoption, governance, and continuous improvement.

Do you build custom AI agents?

Yes. We build AI agents around your workflows, knowledge, systems, and business requirements. They can connect to your existing technology and operate with the evaluation, controls, observability, and human oversight required for production.

Can you develop or improve AI models?

Yes. We can fine-tune, post-train, evaluate, and optimize models for your domain, data, use cases, and performance requirements. We can also help determine when an existing model is the better choice and how to select the right model for each outcome.

Can you work with the AI technology and platforms we already use?

Yes. We work across leading models, cloud providers, data platforms, AI applications, and enterprise systems. Our approach is platform-flexible, so we can build around your existing investments and introduce new technology only where it creates clear value.

Do you provide ongoing AI managed services after deployment?

Yes. We operate and continuously improve AI systems after launch, including evaluation, performance monitoring, model and knowledge updates, human review, exception handling, cost optimization, security, and governance.

The #1 AI Services Company in the World for Agent Creation

Products and services across every layer of the AI stack for organizations and individuals.

Speak With an AI Leader Now

Toptal Frontier AI Stack

AI Strategy
Set direction for advanced AI programs: agent and capability roadmaps, data and organizational readiness, and agentic architecture design for frontier AI.
- Model Fine-tuning Strategy
- Model Stamping
- AI Adoption Readiness Assessment
- Evaluation & Benchmarking Strategy
- Post-training Data Strategy
AI Solutions and Services
Build and validate advanced AI systems: agentic applications, model adaptation and post-training, safety and evaluation assessments, and a fine-tuning platform that runs on-premises.
- AI Safety and Red-team Eval
- Synthetic Data Generation
- RL Services
- Evaluation Coverage Assessment
- Domain-specific Performance Eval
- Supervised Fine-tuning
- Foundation Model Training
AI Partnerships
A curated ecosystem of frontier models and tooling partners, selected and integrated for each workload, balancing quality, reliability, latency, and cost.
- AI Partnerships
AI Talent
Access on-demand AI specialists and tailored engineering teams to design, build, and scale advanced AI systems.
- ML Engineers
- AI Product Managers
- Data Engineers
- AI Researchers
- AI Solution Architects
- AI Eval Program Managers
- MLOps and LLMOps Engineers
- Enterprise Architects
- Evaluation Engineers
AI Platforms
Build and deploy operating environments that connect models, agents, data, and workflows, turning one-off experiments into repeatable, observable capabilities.
- Custom Platform Solutions
- ExpertCrowd
AI Data Service
AI Data Services
Prepare, structure, label, and govern data for model development and adaptation: pre-training and post-training corpora, preference and evaluation sets, and the data agents depend on at run time.
- ExpertCrowd
AI Managed Services
Operate models, agents, and data pipelines with the instrumentation to see drift, the gates to control change, and the data to know why results moved.
- Cyber and AI Security
- AI Governance
- AI Operations & Reliability
- AI Observability & Monitoring
- AI Cost Optimization
- AI Safety & Risk Management
- AI Lifecycle Management

Toptal Enterprise AI Stack

AI Strategy
Define how people and agents divide work: assess organizational and data readiness, design the human-agent operating model and its governance, set the adoption roadmap, and enable the teams running it.
- Human-agent Operating Model
- AI Governance Framework
- Adoption & Enablement
- Enterprise AI Roadmap
- AI Readiness Assessment
- Data & Agent Architecture
AI Solutions and Services
Put AI into production with people in control: develop and integrate agents into existing processes, and define where human judgment is required.
- Human-in-the-Loop Services
- Agent Development
- AI Systems & Process Implementation
- Agentic Workforce Enablement
AI Partnerships
A curated ecosystem of models and tooling partners, selected and integrated for enterprise-grade performance.
- AI Partnerships
AI Talent
On-demand, AI-native specialists who understand enterprise delivery: model and agent engineers, enterprise architects, evaluation and governance leads, and business-function experts.
- Evaluation Engineers
- ML Engineers
- Agent Engineers
- AI Solution Architects
- Data & AI Engineers
AI Platforms
Build and deploy an operating environment that connects models, agents, data, and workflows: enterprise data placed in operational context, agents pre-wired to core systems, and a review cycle that improves them.
- Maestro
- Operationalize
- ExpertCrowd
- ActionTrace
AI Data Service
AI Data Services
Prepare, label, and govern enterprise data for agents and model adaptation, with documented provenance, quality gates, and auditable lineage.
- ActionTrace
- ExpertCrowd
- Data Ontology Design
- Labeling & Grading Programs
- Data Quality & Lineage
- Data Governance & Access
AI Managed Services
Operate enterprise AI in production: service levels and incident response, drift monitoring, controlled model and agent updates, cost and performance tuning, and audit-ready security evidence.
- AI Transformation Office
- AI Governance
- Agent Performance Ops
- AI Operations & Reliability
- AI Observability and Monitoring
- AI Cost Optimization
- Model & Agent Lifecycle Management
- AI Safety & Risk Management
- AI Maintenance & Optimization

In Partnership With:

One Hand to Shake.🤝
Everything You Can Imagine in AI.

Your Company

Toptal Forward-deployed Engineers and Systems Integrators

Toptal AI Workstation

A single interface for working with Toptal AI.

Toptal AI Stack

Robust full-stack AI infrastructure.

AI Commander

Your Organization

Specialist Teams per Component

Agents Team
RL Team
Compute Team
Models Team
Full-stack Team
Monitoring Team
Strategy Team
Integration Team
Operations Team

Frontier-grade AI. Enterprise-ready Integration.

Toptal’s AI operating environment helps enterprises and frontier AI businesses build models, deploy agents, and turn proprietary data into revenue.

Production-ready Agents

Build AI agents that reason, act, and coordinate across tools, teams, and workflows while keeping humans in control.

Fine-tuned Open-weight Models

Design, train, and deploy domain-specific models tailored to your business, data, and performance needs.

Agent, Data, and Model Commercialization

Clean, structure, and package proprietary data into sellable products, services, and new revenue streams.

End-to-End AI Strategy Framework

Drive AI strategy through agent roadmaps, readiness assessments, operating models, governance, adoption, and reinforcement learning.

Toptal’s AI Strategy Framework

Phase 1
Identify Strategic AI Opportunities
Assess priorities, workflows, data, and AI maturity to identify high-impact opportunities.
AI Readiness Assessment
Phase 2
Create Core AI Strategy
Turn priority use cases into a clear AI strategy, roadmap, and success measures.
AI Strategy Roadmap
Phase 3
Design AI Solutions
Architect the models, agents, data flows, and systems required to deliver business outcomes.
AI Solution Architecture
Phase 4
Deploy AI Across the Business
Embed AI into workflows with the enablement, training, and governance needed for adoption.
AI Deployment & Enablement Plan
Phase 5
Optimize AI Performance
Monitor cost, quality, risk, and performance to improve AI in production.
AI Operations Dashboard
Continuous Loop
Phase 6
Drive Continuous AI Innovation
Use performance insights to expand use cases and compound AI value over time.
AI Opportunity Backlog

Example Outputs

Agent Roadmap Development
Agent and Data Readiness Assessments
Human-agent Operating Model Design
Agentic Architecture and Framework Design
AI Adoption and Enablement
AI Governance Frameworks
Foundation Model Development
Reinforcement Learning Workshops
Agent and Model Selection Frameworks

World-class AI Solutions and Services

End-to-end AI development and transformation, spanning foundation models, agents, implementation, optimization, RLHF, and agent-driven workforce enablement.

Production-ready Agents
Authorization Agent
+60%
Faster Authorization
Incident Response Agent
+67%
Faster Resolution
Contract Review Agent
+80%
Productivity Gain
Fine-tuned Foundation Models
Predictive Maintenance Model
+26%
Accuracy Increase
Churn Prediction Model
+19%
Retention Lift
Demand Forecasting Model
+22%
Reliability Gain
Data Commercialization
Frontier-lab Licensing
+$2M
Generated Per Year
Subscription Revenue
+$500K
Increase Per Year
Benchmark Licensing
+$1M
Generated Per Year

Industry-leading AI Partnerships

A curated ecosystem of models and technology partners, configured and managed for enterprise-grade performance and frontier-scale innovation.

Frontier-caliber AI Talent

On-demand frontier AI specialists and engineering teams that design, build, and scale foundation models, multimodal systems, agents, and AI infrastructure.

AI-first Architecture and Proprietary Platforms

Purpose-built platforms designed to solve specific AI challenges across models, agents, data, and workflows.

Best-in-Class AI Data Services

Prepare, structure, label, and align data for AI agents, foundation models, enterprise applications, and data commercialization using best-in-class methods and tooling.

Managed Services for Frontier-grade AI

Continuously operate, test, secure, and improve models, agents, data, and RAG systems in production.

AI Strategy

End-to-End AI Strategy Framework

Drive AI strategy through agent roadmaps, readiness assessments, operating models, governance, adoption, and reinforcement learning.

Toptal’s AI Strategy Framework

Phase 1
Identify Strategic AI Opportunities
Assess priorities, workflows, data, and AI maturity to identify high-impact opportunities.
AI Readiness Assessment
Phase 2
Create Core AI Strategy
Turn priority use cases into a clear AI strategy, roadmap, and success measures.
AI Strategy Roadmap
Phase 3
Design AI Solutions
Architect the models, agents, data flows, and systems required to deliver business outcomes.
AI Solution Architecture
Phase 4
Deploy AI Across the Business
Embed AI into workflows with the enablement, training, and governance needed for adoption.
AI Deployment & Enablement Plan
Phase 5
Optimize AI Performance
Monitor cost, quality, risk, and performance to improve AI in production.
AI Operations Dashboard
Continuous Loop
Phase 6
Drive Continuous AI Innovation
Use performance insights to expand use cases and compound AI value over time.
AI Opportunity Backlog

Example Outputs

Agent Roadmap Development
Agent and Data Readiness Assessments
Human-agent Operating Model Design
Agentic Architecture and Framework Design
AI Adoption and Enablement
AI Governance Frameworks
Foundation Model Development
Reinforcement Learning Workshops
Agent and Model Selection Frameworks

Toptal’s Total AI Framework

The operating environment needed to build, deploy, improve, and scale AI across enterprises and frontier labs.

All Industries

AI Impact

Agent, Data, and Model Commercialization

Frontier Lab
Licensing

Packages proprietary datasets for licensing to leading AI labs.

+$2M

Generated Per Year

Subscription
Revenue

Commercializes continuously refreshed data through recurring subscription products.

+$500K

Increase Per Year

Benchmark
Licensing

Licenses evaluation datasets for model testing and validation.

+$1M

Generated Per Year

Fine-tune Model
Royalties

Monetizes training data through royalties tied to model usage.

+$4M

Generated Per Year

Synthetic Data
Licensing

Licenses privacy-safe synthetic datasets for model training and testing.

+$1.5M

Generated Per Year

Data API
Subscriptions

Delivers proprietary data through APIs with recurring paid access.

+$750K

Generated Per Year

Production-ready agents

Authorization
Agent

Captures clinical documentation, prepares authorization packets, and escalates exceptions.

+60%

Faster Authorization

Incident
Response Agent

Triages alerts, executes approved runbooks, and escalates unresolved incidents.

+67%

Faster Resolution

Contract
Review Agent

Pulls borrower data, runs risk checks, documents decisions, and routes exceptions to underwriters.

+80%

Productivity Gain

Procurement
Agent

Monitors suppliers, identifies lead-time risks, and protects production continuity.

+31%

Efficiency Gain

Warranty
Claims Agent

Validates claims, checks records, and routes complex cases to adjusters for review.

+55%

Claims Throughput

Personalized
Promotions Agent

Segments customers, creates offers, and triggers promotions based on user behavior.

+60%

Offers Redeemed

Fine-tuned Open-weight Models

Predictive
Maintenance

Forecasts failures and prioritizes maintenance before operational disruption.

+26%

Accuracy Increase

Churn Prediction
Model

Identifies at-risk customers and recommends targeted retention actions.

+19%

Retention Lift

Demand
Forecasting

Combines historical and market signals to improve demand planning.

+22%

Forecast Reliability

Clinical
Trial Model

Evaluates trial data, detects anomalies, and accelerates evidence validation.

+60%

Review Speed

Fraud Detection
Model

Detects suspicious activity and prioritizes high-risk cases for review.

+34%

Detection Accuracy

Pricing
Optimization

Analyzes demand and customer behavior to optimize pricing and maximize margins.

+18%

Margin Lift

Agent, Data, and Model Commercialization

Frontier Lab
Licensing

Packages proprietary datasets for licensing to leading AI labs.

+$2M

Generated Per Year

Subscription
Revenue

Commercializes continuously refreshed data through recurring subscription products.

+$500K

Increase Per Year

Benchmark
Licensing

Licenses evaluation datasets for model testing and validation.

+$1M

Generated Per Year

Fine-tune Model
Royalties

Monetizes training data through royalties tied to model usage.

+$4M

Generated Per Year

Synthetic Data
Licensing

Licenses privacy-safe synthetic datasets for model training and testing.

+$1.5M

Generated Per Year

Data API
Subscriptions

Delivers proprietary data through APIs with recurring paid access.

+$750K

Generated Per Year

Production-ready agents

Authorization
Agent

Captures clinical documentation, prepares authorization packets, and escalates exceptions.

+60%

Faster Authorization

Incident
Response Agent

Triages alerts, executes approved runbooks, and escalates unresolved incidents.

+67%

Faster Resolution

Contract
Review Agent

Pulls borrower data, runs risk checks, documents decisions, and routes exceptions to underwriters.

+80%

Productivity Gain

Procurement
Agent

Monitors suppliers, identifies lead-time risks, and protects production continuity.

+31%

Efficiency Gain

Warranty
Claims Agent

Validates claims, checks records, and routes complex cases to adjusters for review.

+55%

Claims Throughput

Personalized
Promotions Agent

Segments customers, creates offers, and triggers promotions based on user behavior.

+60%

Offers Redeemed

Fine-tuned Open-weight Models

Predictive
Maintenance

Forecasts failures and prioritizes maintenance before operational disruption.

+26%

Accuracy Increase

Churn Prediction
Model

Identifies at-risk customers and recommends targeted retention actions.

+19%

Retention Lift

Demand
Forecasting

Combines historical and market signals to improve demand planning.

+22%

Forecast Reliability

Clinical
Trial Model

Evaluates trial data, detects anomalies, and accelerates evidence validation.

+60%

Review Speed

Fraud Detection
Model

Detects suspicious activity and prioritizes high-risk cases for review.

+34%

Detection Accuracy

Pricing
Optimization

Analyzes demand and customer behavior to optimize pricing and maximize margins.

+18%

Margin Lift

AI Impact

Authorization Agent

+60%Faster Authorization

Incident Response Agent

+67%Faster Resolution

Contract Review Agent

+80%Productivity Gain

Procurement Agent

+31%Efficiency Gain

Warranty Claims Agent

+55%Claims Throughput

Personalized Promotions Agent

+60%Offers Redeemed

AI Workstation

AI platform

Agent & model activation

Build and run agents and models.

AI-ready data

Data Artifacts

Structured data and assets.

Ontology

Map your organization.

Data processing

Data Virtualization

Connect, govern, and access your data.

Data Capture

Activity & processes

See how work gets done.

Operations

Map your organization.

Continuous improvement

Learn from feedback.

Sandbox

Test before launch.

Expert-in-the-Loop

Humans in control.

All the compute power you need.

Toptal’s Total AI Framework

Outcome

Outcomes

AI Platform Layer

Agent and Model Activation

Create, manage, and build foundational models and agents.

AI-ready Data Layer

Data Artifacts

Structure data outputs and assets across the organization.

Ontology

Map how your organization works.

AI Data Processing

Data Virtualization

Manage, govern, and scale data within a structured framework.

Activity and Process Capture

Capture real work activity across people, systems, and workflows.

Operational Capture

Map how the organization works: roles, processes, systems, policies.

Training and Experimentation Environment

Continuous Optimization Cycle

Reinforcement Learning

Improve decision-making through continuous feedback and learning.

Sandbox Environment

Test and validate AI solutions before going live.

Expert-in-the-Loop

Maintain AI performance through system monitoring and human-in-the-loop support.

All the computing power needed for inference, reinforcement learning, and sandbox environments.

AI Expertise You Can Count On

Built on 15 years of expertise in product innovation, technology scaling, and world-class talent, Toptal AI helps organizations build production-ready AI agents, fine-tune foundation models, commercialize data, and accelerate AI adoption at scale.

35,000++

Clients Served

30,000++

Total Vetted Professionals

2,000++

AI & Big Data Experts

250,000+

AI & Data Project Hours Delivered

Provider-agnostic Recommendations

Leading AI Platforms. Expert Guidance.

You shouldn't be locked into one vendor. Our AI experts build provider-agnostic systems and make recommendations based on your specific business needs.

Anthropic

Our point of view on Anthropic is shaped by 847 vetted experts shipping production systems across 38 countries.

Gabriel Tavares de Souza

Verified Expert in Engineering

Principal AI Engineer

Gabriel is a principal engineer and AI systems architect with 19+ years of experience delivering production-grade software across AI, cloud, and distributed systems.

LLMRAG

Previously at

Hire Gabriel

Vetted Experts: 847
Projects Delivered: 312
Countries: 38

4 Top Google AI Minds Just Left—Their  Next Move Defines The Talent War

Aug 7, 2026See the Forbes Article

Toptal has talent that hail from
AI leaders like OpenAI, Anthropic, DeepMind, and Palantir

How We Build and Continuously Improve AI Solutions

Daily Team Meeting
Our team reviews your operations and developments in the AI market, prioritising what to build next.
Execute Daily Plan
Our specialist teams build and improve your AI solutions based on the day’s priorities and your business goals.
Test and Deploy to Sandbox
We test each solution in a sandbox and measure its performance against defined targets before launch.
Deploy to Production
We deploy validated solutions and use real-world results to guide the next round of improvements.

World-class Frontier AI and Enterprise AI Talent

Work with best-in-class AI talent to build frontier models and deploy enterprise AI systems and agents.

Drive AI model development across dataset acquisition, algorithms, training, evaluation, and deployment.

AI Researchers
Data Scientists
Data Engineers
ML Engineers
ML, LLM & RL Ops
AI Evaluation Engineers

Case Studies

Real AI Outcomes, Delivered End-to-End

See what clients have achieved with Toptal’s AI capabilities and talent.

Toptal AI powers expert-validated datasets to advance financial reasoning in GenAI.

Meridian, an AI-powered financial modeling company backed by Andreessen Horowitz, partnered with Toptal AI to build a large-scale expert-driven pipeline for training and evaluating AI financial reasoning.

The Challenge

Meridian needed a scalable way to produce thousands of validated, machine-readable financial models under a strict quality framework to enhance the accuracy of AI-powered analyses.

Solutions

Workforce Management

Toptal AI coordinated 100+ financial analysts to produce high-quality financial models and analytical outputs for AI training.

Calibration for Consistent Evaluation

Toptal AI’s financial analysts implemented expert review and calibration to maintain consistent evaluation standards and quality.

Outcome

Training Data for Financial Reasoning

The engagement produced high-quality preference and evaluation datasets used to train and benchmark AI systems on complex financial reasoning tasks.

Scalable Datasets

By converting professional financial modeling expertise into structured training data, the client gained scalable datasets capable of supporting model development, evaluation, and improvement in generating accurate financial analyses.

Client

Meridian is an AI-powered accounting platform that automates the full month-end close, helping accounting firms increase capacity and accelerate financial reporting.

Delivered Services

Machine Learning Development Services
Data Engineering Services
Data Science Services
Big Data Services

Read Full Case Study

Toptal AI converts analyst insights into structured, AI-ready financial intelligence.

Sterling Labs, a fintech startup backed by Ribbit, partnered with Toptal AI to hire 60+ vetted equity researchers and develop a scalable expert annotation platform that transformed unstructured financial disclosures into AI-ready knowledge for LLMs and financial research applications.

The Challenge

Sterling Labs needed a reliable way to transform complex financial filings into structured, high-quality datasets for LLMs and AI-powered financial analysis, but existing labeling vendors couldn't provide the financial expertise or consistency required at scale.

Solutions

Financial Knowledge Extraction

Toptal AI developed a structured annotation platform that extracted standardized insights from key filings, transcripts, presentations, and public disclosures. A hierarchical financial taxonomy ensured consistent capture of KPIs, business drivers, and risks across companies.

Scalable Human-in-the-loop Financial Intelligence

Toptal AI built a scalable human-in-the-loop workflow that coordinated 60+ vetted equity researchers, matched analysts to companies based on their domain expertise, and managed sourcing, quality assurance, and structured annotation for high-confidence, AI-ready datasets.

Outcome

AI-ready Financial Intelligence

The platform transformed unstructured corporate disclosures into standardized, machine-readable financial knowledge that powered ChatGPT-based summarization, benchmarking, comparative analysis, and decision-support applications.

Enterprise-scale Knowledge Infrastructure

By combining expert financial analysis with structured taxonomies and scalable annotation workflows, Toptal AI enabled Sterling Labs to launch its AI-powered financial research platform in beta and expand analysis across the S&P 500 and beyond.

Client

Sterling Labs is a leading network of environmental testing laboratories, delivering accurate, high-integrity testing solutions for water, air, soil, waste, and other environmental services.

Delivered Services

Data Engineering Services
Data Science Services
Machine Learning Development Services
Big Data Services

Read Full Case Study

Toptal Ranked #1 Most Reliable Professional Services Company in America

Newsweek and Statista’s rankings were based on an independent survey of more than 2,400 decision-makers at Fortune 500s.

Methodology for the Rankings

Newsweek and Statista Most Reliable Companies rankings
Company	Rank
Microsoft	1
IBM	2
Amazon	3
Toptal	11
Adobe	12
Accenture	33
Deloitte	39
Cognizant	66
McKinsey & Company	80
KPMG	101

Highest ranked across all industries

Other professional services

See the Most Reliable Companies in America

Highest ranked across all industries

Other professional services

How It Works

Six Phases. From AI Ambition to Impact.

Toptal’s AI capabilities run on a repeatable six-phase process, tested and proven across 300+ AI initiatives.

Phase 1
Discover
You tell us your AI ambitions and goals
Phase 2
Define
We architect the solution and assemble the team
Phase 3
Develop
We build production-grade systems
Phase 4
Deploy
We test in a sandbox before going live
Phase 5
Manage
We monitor and maintain your AI system
Phase 6
Innovate
We continuously update and refine your AI system

Get Started

Put production AI on the calendar this quarter.

Tell us what you’re building. We’ll define the right approach together.

Speak With an AI Leader Now See Case Studies

Testimonials

Why Clients Choose Toptal AI Over Consultancies and Platforms.

Toptal AI helped us operationalize benchmark development from environment design through task creation and evaluation. Their subject matter experts translated real-world professional workflows into rigorous benchmark environments, reference outputs, and evaluation frameworks for measuring model performance on complex knowledge work.

Jay Ram, CEO

backed by

Domain consistency is the bottleneck for reliable inference. Toptal AI assembled more than 60 equity researchers to create structured financial data and map latent domain logic for our AI-powered trading research platform.

Alek Safarov, CEO

backed by

Toptal AI has been an excellent partner, giving us the experts we needed to go beyond standard benchmarks and dig into where models were actually struggling to reason through complex tasks. Those insights fed directly into our post-training work, helping us strengthen how models handle complex reasoning.

Daniel Rodrigues, Data Talent Procurement

servicing

Related Expertise

Tackle your most important AI initiatives with a full suite of AI competencies.

LLM Development
RAG Engineers
AI Agent Engineers
AI Evaluation Engineers
Prompt Engineering
AI Red Teamers
Generative AI Development
AI Security Engineers
Reinforcement Learning Engineers
MLOps
AI Transformation
NLP Engineers
Agentic AI Engineers
Intelligent Automation
MLOps Engineers
AI Chatbot Development
Deep Learning Engineers
Computer Vision Engineers
Machine Learning Development
LLM Evaluators
Vector Database Engineers
AI Product Managers

LLM Development
RAG Engineers
AI Agent Engineers
AI Evaluation Engineers
Prompt Engineering
AI Red Teamers
Generative AI Development
AI Security Engineers
Reinforcement Learning Engineers
MLOps
AI Transformation
NLP Engineers
Agentic AI Engineers
Intelligent Automation
MLOps Engineers
AI Chatbot Development
Deep Learning Engineers
Computer Vision Engineers
Machine Learning Development
LLM Evaluators
Vector Database Engineers
AI Product Managers

Blog

Industry-leading AI Insights

Strategy and engineering perspectives from the Toptal experts shipping AI systems to market today.

AI Training Data & Fine-Tuning

Fine-tuning LLMs for Your Industry: Optimal Data Labeling Strategies

LLMs have a vast knowledge base, but training them with domain-specific data can extend their capabilities to specialized industries and tasks. This article delves into data labeling for fine-tuning and includes a tutorial for preparing and fine-tuning an LLM with Label Studio.

By Jedrzej Kardach

5 Years of Experience in Engineering

Verified Expert in Engineering

Data Science and Databases

A Deep Dive Into Reinforcement Learning

By Adam Stelmaszczyk

Verified Expert in Engineering

Data Science and Databases

Exploring Supervised Machine Learning Algorithms

By Vlad Miller

Verified Expert in Engineering

Machine Learning Engineering

The Many Applications of Gradient Descent in TensorFlow

By Alan Reiner

Verified Expert in Engineering

Machine Learning Engineering

Adversarial Machine Learning: How to Attack and Defend ML Models

By Pau Labarta Bajo

Verified Expert in Engineering

Toptal AI Research and Reports

What’s Shaping the Future of AI

A curated collection of research, benchmarks, and perspectives from the people and organizations pushing AI forward.

Research

AutoDesign: Meta-harness Optimization for Long-horizon Agentic Design

A new research framework explores how AI agents can improve their own workflows through iterative feedback. The results suggest that better scaffolding and system design can sometimes outperform simply moving to a larger model.

Read the Research

Source:

Article

MIT AI Report Calls for Alternative Grading, More Social Learning

Artificial intelligence is upending the MIT education, a committee has found. Students, instructors, and administrators will need to revisit nearly every part of that experience to respond to it.

Read the Article

Why we selected this article

This Inside Higher Ed article examines how AI is reshaping technical education, from how students learn to how to measure mastery. A committee from influential technical institution MIT has recommended alternative grading and more social learning, signaling changes to the way the next generation of tech and AI experts will be trained.

Curated by

Bas Steunebrink

AI Researcher20+ Years of Experience

Bas researches artificial general intelligence and AI safety, with a focus on systems that learn from experience. A co-founder of NNAISENSE and former IDSIA researcher, he has explored how AI can develop safe, reliable behavior.

Education: PhD, Artificial Intelligence
Previously at

Source:

Report

The 2026 AI Index Report

The AI Index offers one of the most comprehensive, data-driven views of artificial intelligence. Recognized as a trusted resource by global media, governments, and leading companies, the AI Index equips policymakers, business leaders, and the public with rigorous, objective insights into AI’s technical progress, economic influence, and societal impact.

Read the Report

Top Takeaways

AI capability is not plateauing. It is accelerating and reaching more people than ever. Industry produced over 90% of notable frontier models in 2025, and several of those models now meet or exceed human baselines on PhD-level science questions, multimodal reasoning, and competition mathematics. On a key coding benchmark—SWE-bench Verified—performance rose from 60% to near 100% of meeting the human baseline in a single year. Organizational adoption reached 88%, and 4 in 5 university students now use generative AI.
The U.S.-China AI model performance gap has effectively closed. U.S. and Chinese models have traded the lead multiple times since early 2025. In February 2025, DeepSeek-R1 briefly matched the top U.S. model, and as of March 2026 Anthropic’s top model leads by just 2.7%. The U.S. still produces more top-tier AI models and higher-impact patents, while China leads in publication volume, citations, patent output, and industrial robot installations. South Korea stands out for its innovation density, leading the world in AI patents per capita.
The United States hosts the most AI data centers, with the majority of their chips fabricated by one Taiwanese foundry. The United States hosts 5,427 data centers, more than 10 times any other country, and it consumes more energy than any other country. A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan—though a TSMC-U.S. expansion began operations in 2025.

Source:

Article

New Method Enables AI for Safety-critical Situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

Read the Article

MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems.

In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical, or task-specific requirements, known as hard constraints.

The researchers developed a method that helps generative models meet these strict requirements without sacrificing the quality of their outputs.

The key to their technique is to give the model more freedom during the generation process and enforce hard constraints on the final output, rather than at every intermediate step.

In experiments spanning robotics, control of physical processes, and computer vision, the new method consistently satisfied the required constraints while identifying better solutions than existing techniques.

This adaptable, plug-and-play technique works at deployment time, so it can be applied to pretrained generative models without retraining them. It can make such models more useful in applications where safety rules, physical laws, or other strict requirements cannot be violated.

Source:

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What makes Toptal AI different?
We help organizations move AI from ambition to validated business outcomes. We combine specialized AI talent, delivery teams, proprietary capabilities, and leading technology partners to build around your data, workflows, systems, and business goals, then continuously evaluate, operate, and improve what gets deployed.
What AI services does Toptal provide?
Toptal AI helps organizations understand how work gets done, capture the activity and knowledge behind it, and turn that context into production AI systems. We combine workflow and process mapping, data and knowledge preparation, agent and model orchestration, evaluation, governance, expert-in-the-loop operations, and managed services to continuously improve performance against validated business outcomes.
Can you support an AI initiative from strategy through production?
Yes. We can support a focused initiative or take ownership across the full life cycle, from identifying the opportunity and designing the solution through building, integration, deployment, adoption, governance, and continuous improvement.
Do you build custom AI agents?
Yes. We build AI agents around your workflows, knowledge, systems, and business requirements. They can connect to your existing technology and operate with the evaluation, controls, observability, and human oversight required for production.
Can you develop or improve AI models?
Yes. We can fine-tune, post-train, evaluate, and optimize models for your domain, data, use cases, and performance requirements. We can also help determine when an existing model is the better choice and how to select the right model for each outcome.
Can you work with the AI technology and platforms we already use?
Yes. We work across leading models, cloud providers, data platforms, AI applications, and enterprise systems. Our approach is platform-flexible, so we can build around your existing investments and introduce new technology only where it creates clear value.
Do you provide ongoing AI managed services after deployment?
Yes. We operate and continuously improve AI systems after launch, including evaluation, performance monitoring, model and knowledge updates, human review, exception handling, cost optimization, security, and governance.

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