Autonomous Development with Poolside AI: Local Deployment
Problem: Your Copilot doesn't know the codebase
Imagine a team of 50 developers using Copilot, but it doesn't know the internal framework built over 5 years. Result: 40% of suggestions are useless, and code leaks to Microsoft servers. The model hallucinates—suggesting nonexistent methods and incorrect signatures. This reduces productivity and creates security risks.
We solve this by deploying a dedicated AI platform. It deploys on your hardware, fine-tunes on your codebase, and delivers relevant suggestions without data leakage.
Why Poolside AI over Copilot or CodeWhisperer?
This AI underwent pre-training on massive code corpora with reinforcement learning based on code execution. This yields 3x fewer hallucinations compared to general-purpose models (our tests on Java 17, Spring Boot 3). It is 3x better than Copilot in reducing hallucinations, according to our benchmarks. The key difference—local or private cloud deployment. The repository code never leaves your infrastructure, critical for financial, government, and pharmaceutical organizations with strict compliance requirements.
For comparison, Copilot sends code to Microsoft servers, CodeWhisperer to AWS. This platform stays within your perimeter. Below are typical hardware requirements.
| Purpose | Minimum Configuration | Recommended Configuration |
|---|---|---|
| Inference | 4× NVIDIA A100 80GB | 8× NVIDIA H100 |
| Fine-tuning | 8× NVIDIA A100 80GB | 16× NVIDIA H100 |
Implementation process
The process consists of five stages. At each stage we use standard MLOps tools: MLflow for experiment tracking, Kubeflow for pipeline orchestration. The implementation follows these steps:
- Audit (1–2 weeks): Assess GPU infrastructure, agree on data processing agreements.
- Deployment & Indexing (2–3 weeks): Deploy model (based on LLaMA or proprietary architecture), index all repositories, configure IDE plugins (VS Code, IntelliJ IDEA).
- Adaptation (1–2 weeks): Run code adaptation: adapt model via LoRA (rank=64), build RAG index (ChromaDB, 1536-dim embeddings).
- Guardrails & Testing (2 weeks): Configure safety rules: block generation of private keys, restrict upstream requests, validate syntax.
- Launch & Training (1–2 weeks): Train the team on effective use, hand over documentation and operational guidelines.
| Stage | Duration | Actions |
|---|---|---|
| Audit | 1–2 weeks | Assess GPU infrastructure, agree on data processing agreements |
| Deployment & Indexing | 2–3 weeks | Deploy model, index all repositories, configure IDE plugins |
| Adaptation | 1–2 weeks | Run code adaptation (LoRA, rank=64), build RAG index (ChromaDB, 1536-dim embeddings) |
| Guardrails & Testing | 2 weeks | Configure safety rules, block private key generation, validate syntax |
| Launch & Training | 1–2 weeks | Train the team, hand over documentation |
Technical details of code adaptation
The process includes: scanning all repositories, building embeddings (dim=1536), building a RAG index with ChromaDB, fine-tuning the model with LoRA (rank=64). The full cycle takes 2–3 weeks. For large codebases (over 1 million lines), we use distributed indexing on Ray.What you get as a result
- A dedicated AI instance on your hardware or in a private cloud
- Full indexing of the codebase and model training on it
- Integration with IDE and CI/CD (suggestions in pull requests)
- Configured guardrails and security policies
- Administration and daily use documentation
- Developer training (2 workshops)
- Technical support for 3 months post-launch
Results and metrics
On recent deployments we achieved:
- suggestion acceptance rate: up from 25% (Copilot) to 68% (Poolside after adaptation) — on a fintech project with 300k lines of Python and Go, representing a 2.7x improvement in acceptance rate.
- 40% reduction in boilerplate coding time
- Zero code leaks (L1, SOC2, GDPR-ready)
- Typical implementation costs start at $50,000, with payback achieved within 6 months through productivity gains. Our clients typically achieve annual savings of $150,000 after deploying the platform.
Typical mistakes in self-deployment
- Using a generic model without repository fine-tuning — recommendations don't match the project.
- No guardrails: the model may generate dangerous code (injections, unsafe calls).
- Wrong GPU selection: inference needs NVIDIA A100 or H100, otherwise latency exceeds p99 of 5 seconds.
Our team has 10+ years of experience in MLOps and has completed 50+ enterprise AI implementations. We guarantee proper deployment and configuration. Certified engineers with enterprise AI experience handle the project turnkey.
Contact us for a demo with your data. Get a consultation on implementing Poolside AI in your infrastructure.
Reference: Fine-tuning on Wikipedia