Raise your model's reasoning ceiling

Reasoning traces and demonstrations authored by domain experts, graded before they shape your model.
Raise your model's reasoning ceiling

Frontier

AI Lab

Leading

Smartphone Maker

Major

Social Platform

Top GenAI

Assistant Maker

Leading

Search Engine

Leading Global

Ecommerce

Frontier

Robotics Studio

Iconic

Smart Eyewear

World-Leading

Creative AI Platform

Premium

Automotive AI

Top-20

US Mortgage Lender

Major

Crypto Trading Platform

Global

Banking-as-a-Service

Emerging

BNPL Fintech

Global

Card Issuer

Tier-1

FinCrime Portfolio
Market Forces

Why demonstration quality is now a model capability question

Demonstration errors don't stay in the training set. They replicate into model reasoning at every inference.
Model collapse
Model collapse

2X

Models trained on AI-generated content lose quality at 2X the rate, and the degradation compounds with each training cycle that recycles synthetic demonstrations.
Benchmark Contamination
Benchmark Contamination

42%

of recent AI evaluation results were inflated because test questions leaked into training data. CoT demonstrations sourced from public benchmarks carry the same contamination risk.
Traning Cost
Traning Cost

80%

of AI project time is spent on data preparation and labeling. Expert-authored SFT demonstrations reduce downstream iteration cycles by getting the reasoning right the first time.
Proof in production

Numbers from programs already in production

Demonstration programs already shaping how production models reason.

≥5%

absolute benchmark uplift on corporate-finance reasoning, with 4-step to 25-step reasoning chains  grounded in annual-report tables, charts, and financial statements.

20K+

prompts and expert-authored responses across reasoning, coding, audio, multimodal, and instruction categories with model evaluation, response ranking, and 0% error rate for a world-leading search provider.

40%

effort reduction with 30% knowledge transformation, via instruction-data curation and SFT demonstrations for a global banking and payments platform serving regional banks.
Across the GenAI lifecycle

Expert demonstrations across the training lifecycle

Different model stages demand different demonstration types, authoring profiles, and QA discipline. The wrong demonstrations at the wrong phase don't just fail, they actively mislead.
Phase 01

Pre-training

Pre-training demonstration design sets the taxonomy: what reasoning patterns to teach, what domain concepts to represent, and what output schema makes demonstrations production-ready.
Pharma clinical trial extraction
We designed a 47-field extraction schema for 200-page FDA submissions with MedDRA coding.
Phase 02

Fine-tuning

Fine-tuning is where SFT demonstrations shape model behavior. Expert authors construct reasoning chains and instruction-response datasets, with multi-layer QA enforcing schema compliance and factual accuracy.
Payer medical document extraction
We processed 50,000+ medical records across 47 structured fields at 98.2% accuracy.
Phase 03

Post-training

Post-training uses CoT demonstrations to evaluate and refine model reasoning through RLHF cycles. Evaluators grade chain-of-thought outputs for logical validity and domain correctness.
Autonomous prior authorization agent
We reduced agent runtime from 14 minutes of manual work to 4 minutes with CoT-graded PA logic.
Phase 04

Deployment

Deployed models encounter document types and edge cases not in the original training set. Continuous expert demonstrations keep the model accurate as real-world distribution shifts.
Mortgage application processing
99.4% field-extraction accuracy on 23-document batches across 40+ document types.
Program Spotlight

Expert SFT demonstrations across STEM domains

Expert-authored demonstrations across physics, chemistry, and medicine in one program.
Where it applies

Where expert demonstrations drive production outcomes

Where reasoning displaces guessing

AI labs and foundational models

Frontier models that reason across STEM, code, and multilingual domains need expert-authored CoT traces, not recycled synthetic outputs, to clear Arena-style evaluation thresholds.

Technology

On-device assistants, gesture systems, and spatial computing models ship faster when SFT demonstrations are authored by specialists who understand the hardware, the modality, and the user interaction.

Robotics

Vision-language-action models won't generalize manipulation or navigation tasks without domain-expert demonstrations grounding language commands to physical actions and sensor inputs.

Banking and financial services

FinCrime, credit, and compliance models need CFA and FRM-credentialed authors writing multi-step reasoning chains that hold up under audit and regulatory review.

Healthcare

Clinical AI that clears hospital credentialing requires demonstrations authored by certified medical professionals across diagnostic, pharmacological, and claims domains.

Retail and commerce

Product discovery, demand forecasting, and catalog agents depend on SFT demonstrations that encode real operational procedures, not generalist reasoning.
How we deliver

Expert demonstrations authored once, graded three times

Authored by domain experts, then graded three times before anything trains your model.
Workforce

Hundreds of

domain specialists

PhDs, MDs, engineers, and domain scientists author the demonstrations, not anonymous click-workers, and each specialist is credentialed and calibrated to the client's domain.
Quality Assurance

Multi-layer

verification at every stage

We run expert authoring, an independent reviewer pass, automated schema validation, a factual-accuracy check, and client-gate sign-off before any batch ships.
Platform

Agentic AI studio

built for scale

We orchestrate authoring, QA, and delivery through the Agentic AI Studio platform, with real-time quality dashboards and governance built in.
INSIGHTS

Latest from the Firstsource team

Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Cuban is half right. The services that survive AI are the ones AI cannot do without
BLog

Cuban is half right. The services that survive AI are the ones AI cannot do without

Why the services that survive AI are those it cannot do without judgment—exploring where human expertise remains essential in an increasingly automated.
Models double their attention span every four months. Are you doubling your demonstrations?
BLog

Models double their attention span every four months. Are you doubling your demonstrations?

As AI context windows double every four months.
SWE-bench is a tournament. Your codebase is a job
BLog

SWE-bench is a tournament. Your codebase is a job

Why SWE-Bench is a tournament and your codebase is a job—closing the gap between competitive AI coding benchmarks and real-world software engineering.
Leading tech company trains and enhances voice assistant with AI/ML data
Case Study

Leading tech company trains and enhances voice assistant with AI/ML data

This case study is about how a leading tech company trained and enhanced its voice assistant with Artificial Intelligence/Machine Learning data.
AI lab raises corporate finance reasoning benchmark
Case Study

AI lab raises corporate finance reasoning benchmark

Corporate-finance reasoning pilot for a Frontier AI Lab ” 4-25 step reasoning chains grounded in annual reports, schema-compliant outputs, targeting measurable benchmark uplift.
Global hyperscaler lands 6-language IIT/JEE test bank
Case Study

Global hyperscaler lands 6-language IIT/JEE test bank

IIT/JEE Mains content authored, validated, and localized for a Global Hyperscaler ” English plus 5 Indian languages, with expert-team QA across all languages.
Contact US

Talk to an SFT lead

Tell us the domain, the reasoning task, and the model stage. A program lead replies inside one business day.
  • Talk to a real program lead, not a sales SDR.
  • Sample demonstration set returned in 5 to 10 business days.
  • Rubric and schema co-design included in scoping.
  • NDA before any model or data exchange.