Repo-Specific Post-Training
We adapt open coding models to your internal APIs, architecture boundaries, and engineering conventions using synthetic repo tasks.
Enterprise AI Reliability
Repo-Aligned Coding Agents For Complex Enterprise Repositories
Experts still have to catch confident agent errors
Noam Brown shared that coding agents helped him iterate faster, but they also made confident, repeated mistakes that required expert intervention to resolve.
In a poker-solver build, agent outputs looked plausible but were still wrong in key cases, showing how overconfidence becomes risky in specialized technical work.
Open sourceEnterprise teams are moving from copilots to agents, but generic agents break down on large private codebases. They miss hidden repo rules, generate plausible-but-wrong changes, and increase review and security burden. What's missing is a repo-aligned adaptation and verification layer that turns output into trusted, shippable changes.
Best fit
1,000+ engineer organizations
Codebase
Long-lived, high-coupling repositories
Deployment
VPC or on-prem control boundary
Solution
A practical model-plus-harness stack for large, private, long-lived codebases.
Continuous Verification Harness
Every output is evaluated against CI, tests, quality checks, and security controls so trust is based on evidence, not optimism.
Enterprise Deployment Controls
Deploy in customer VPC/on-prem with governance, audit logs, and phased rollout so adoption scales safely across teams.
Proof Through Aryabhata 1.0
Our strength is reinforcement-learning-based post-training on domain-specific data. The result is compact models that can outperform larger frontier systems on targeted tasks.
Thesis: compact RL post-training beats generic scale
7B-class specialized model vs frontier baselines
Our previous website states the operating thesis directly: specialized compact LLMs trained with reinforcement learning can outperform frontier models at a fraction of the cost.
Published benchmark edge on GSM8K
94.8 (Aryabhata) vs 90.1 (o4-mini) vs 85.1 (Gemini 2.5 Flash)
In Table 3 of the Aryabhata paper, the model records 94.8 on GSM8K, above listed proprietary baselines while remaining in the compact-model regime.
In-distribution reliability with token efficiency
JEE Main 2025: 86.0% (Jan), 90.2% (Apr), ~2K tokens/response
The paper reports strong in-distribution accuracy and describes Aryabhata as outperforming evaluated baselines on JEE Main while staying competitive on inference cost.
Plan Your Enterprise Pilot
In one working session, we align on a high-friction workflow, define success criteria, and outline a low-risk rollout plan for your team.
Book Pilot Planning SessionTeam
Two builders focused on making AI coding trustworthy in real enterprise environments.
Sachin
CEO
Published Aryabhatta 1.0. Ex-Samsung, JP Morgan, and Unity. Brings 10+ years of deep reinforcement learning experience.
- Published Aryabhatta 1.0
- Ex-Samsung, JP Morgan, Unity
- 10+ years in RL
Rohith
CTO
Published Table Transformer. Ex-MSR. Brings 10+ years of practical experience applying deep learning to real products.
- Published Table Transformer
- Ex-MSR
- 10+ years in applied deep learning
Resources
Paper
Aryabhata 1.0: RL Post-Training For Compact Reasoning
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.
Open paperFrom Rewards to Reasoning: A Talk By Sachin
Session on how reward signals and RL techniques are used to shape language-model reasoning behavior.
Open talkTraining Large Reasoning Models With RL and Search
Technical deep dive on experimental learning loops for reasoning model training and evaluation.
Open talkFAQ
Quick answers on security, deployment, and measurable outcomes.
How is customer code protected?
AthenaAgent is designed for private deployment in customer-controlled environments with strict access boundaries and auditable activity logs.
How do you prove reliability?
Every generated change runs through a verification harness that checks CI, tests, policy rules, and security tooling before it is trusted.
How does rollout work?
We start with a scoped workflow and expand gradually as measurable quality and cycle-time metrics demonstrate sustained gains.
What ROI should teams expect?
Target outcomes include lower review burden, faster issue resolution, fewer regressions, and clearer governance over agent behavior.
Can this fit existing engineering systems?
Yes. AthenaAgent is designed to integrate with existing CI/CD, security scanners, and review workflows rather than replacing them.