Environment‑Grounded Training
Models learn from tasks, tools, and feedback in situ—not just text patterns but real-world interactions and domain-specific workflows.
Build AI Agents That Actually Work in Production
We deliver specialized, compact LLMs through reinforcement learning—outperforming frontier models at a fraction of the cost.
Aryabhata 1.0: Our First Frontier-Beating Model
Our 7B parameter Aryabhata model outperforms OpenAI's O4 mini and Google's Gemini Flash 2.5 on mathematics benchmarks—designed to serve millions of students at scale.
Your AI Agents Are Failing Because Prompting Isn't Enough
You've tried prompt engineering. You've tested every frontier model. Yet your agents still hallucinate, fail edge cases, and burn through API costs.
The truth? MIT research shows 95% of GenAI projects fail to reach production[1]. General-purpose models trained on internet text weren't designed for your specific domain. No amount of prompting can fix a fundamental training mismatch.
01
Prompt Sensitivity
Minor wording changes and context shifts cause large output variance.
02
Brittle Agent Chains
Multi-step tool use fails silently; errors cascade without guardrails.
03
Non‑Determinism
Same inputs yield different outputs—hard to test and certify.
04
Hidden Costs at Scale
Retries, orchestration and evals inflate latency and spend.
These challenges compound at scale, making traditional approaches unsuitable for production AI systems.
Our Solution: Train to Learn from the Environment
We move beyond prompting. We train agents with reinforcement learning and domain signals so they become robust, testable systems tuned to your environment.
Reward Modeling
Align behavior to domain‑specific KPIs, not generic chat objectives—optimizing for your success metrics, not conversation quality.
Simulator‑in‑the‑Loop
Scale safe exploration before production rollout—test edge cases and failure modes in controlled environments first.
Compact, Specialized Models
Faster, cheaper inference with higher task accuracy—purpose-built efficiency that outperforms general models on your use cases.
On‑Prem/Private Deployments
Preserve privacy and compliance—keep sensitive data within your infrastructure while maintaining full model performance.
Our Process
A clear path from KPIs to reliable production agents.
01
Dataset Analysis & Curation
Step1 of 6
We analyze your data landscape and curate high-quality training sets optimized for your domain.
Comprehensive data assessment and preparation tailored to your specific use case requirements and business objectives.
02
Compute Cost Estimation
Step2 of 6
Transparent pricing with detailed compute projections—no surprise bills.
Clear, upfront cost breakdown with detailed resource planning so you know exactly what to expect throughout the training process.
03
Evaluation Framework Design
Step3 of 6
We help create comprehensive eval sets that measure what actually matters for your use case.
Custom evaluation metrics and test suites designed to validate performance on your specific business requirements and success criteria.
04
RL-Based Model Training
Step4 of 6
Our specialized training pipeline delivers compact models that outperform giants.
Advanced reinforcement learning techniques that create efficient, domain-specific models with superior performance at a fraction of the size.
05
Deployment & Integration Support
Step5 of 6
Seamless integration with your existing infrastructure and workflows.
Full deployment support with comprehensive integration assistance to ensure smooth adoption within your current technology stack.
06
Performance Monitoring & Optimization
Step6 of 6
Continuous monitoring and iterative improvements post-deployment.
Ongoing performance tracking, optimization, and model updates to ensure sustained excellence and adaptation to evolving requirements.
Typical Timeline: 3-6 months from kickoff to production
Where Domain-Specific Models Win
Financial Services
- Risk assessment agents that understand your regulatory environment
- Trading algorithms trained on your market dynamics
Healthcare & Pharma
- Clinical decision support trained on your protocols
- Drug discovery models optimized for your research areas
Education & EdTech
- Personalized tutoring agents that match your curriculum
- Assessment systems aligned with your pedagogy
Manufacturing & Supply Chain
- Quality control agents trained on your product specifications
- Demand forecasting tuned to your market patterns
Led by a Decade-Long RL Expert Who's Built This Before
Sachin Dharashivkar, CEO & Founder
With over a decade of experience building RL agents and simulators at scale, Sachin brings deep expertise from:
JPMC
Samsung Research
Unity
Huawei
Autodesk
UMass Amherst
Key Achievements
- At JPMorgan Chase: Built advanced simulators and RL agents for high-volume equity trading systems
- At Unity: Trained collaborative AI agents that mastered the complex multiplayer game Overcooked
- Technical Leadership: Published research on Aryabhata model, delivered talks at Lossfunk on advanced model training
A decade of RL and simulation engineering across finance, gaming, and research.
For the Technical Leaders: Our Approach
- Architecture: Efficient transformer variants optimized for inference
- Training Pipeline: Custom RLHF with domain-specific reward models
- Evaluation: Comprehensive benchmarking against frontier models
- Integration: REST APIs, Python SDKs, and containerized deployments
Go Deeper Into Our Work
Technical resources and researchFrequently Asked Questions
Ready to Build AI That Actually Works?
Stop wasting resources on unreliable agents. Let's discuss how custom RL training can solve your specific challenges.