Enterprise RAG & AI Search
Semantic search and answer generation over heavily segmented client data. The AI surfaces only what the current user is permitted to see.
Specialist AI Engineering Studio
We help backend-heavy B2B companies ship AI features into real production systems.
Without breaking your architecture, security, cost, or reliability.
We translate generative AI from unstable experiments to reliable features inside your SaaS product. Strict latency control, legacy code integration, semantic caching, and full RBAC support.
$ curl -X POST /api/chat -H "Authorization: Bearer <token>"
[auth] ✓ token validated · tenant: acme-corp
[rbac] ✓ scope: read:kb · filtered: 847 → 12 docs
[cache] ✓ semantic hit · cosine: 0.94 ·
saved: 420ms
[llm] ✓
streamed 312 tok · p99: 180ms ·
cost: $0.002
The Problem
The gap between an "AI Demo" and "Production-Grade AI".
Most agencies sell Python scripts on top of OpenAI API. It works in a sandbox. It breaks when it meets real B2B production.
| Typical AI AgencyThe Demo | Engineering-Led StudioProduction Reality | |
|---|---|---|
| Security & Auth | Single API key. AI has access to all data in the database. | IAM integration. Context-aware filtering. Least-privilege RBAC at the vector DB level. |
| Data Privacy | Raw data (incl. PII) sent to public LLM servers. | PII redaction in-flight. Local model deployment in closed environments. Full GDPR compliance. |
| Performance & Latency | Synchronous calls. User waits 15 seconds for a response. | prefill/decode split, semantic caching, model routing. Async queues (Kafka) for long background tasks. |
| Architecture | Isolated script disconnected from the main codebase. | Seamless AI microservice integration with your legacy Java/.NET monolith via reliable APIs and message brokers. |
| Observability | Black box. Unknown token spend. | Per-request logging. Real-time tracking of latency, token cost, and cache hit rate. |
What We Build
What we engineer for your product.
In-Product Copilots
Specialized assistants embedded in your UI. Copilots that don't just answer questions — they safely call internal APIs to automate user workflows.
Legacy Backend Modernization
Wrapping existing monolithic systems with AI capabilities using the Strangler Fig pattern and data buses. LLM provider failures stay isolated from your core product.
Workflow Automation
Deterministic background pipelines for processing large volumes of unstructured documents, data extraction, and task routing — with error recovery and human review at critical checkpoints.
Engineering Velocity
Integrate Coding Agents into your SDLC.
Increase engineering throughput without expanding headcount. We bring Claude Code and Cursor integration from startup sandboxes into hardened corporate CI/CD pipelines.
> Setup Secure Context Boundaries
Train models on your specific codebase without violating IP security policies. Custom context windows, access-scoped retrieval, and audit logs for every model invocation.
> Automate Testing Pipelines
AI agents that generate unit and end-to-end tests from functional requirements — integrated into your CI so every PR includes coverage before human review.
> Intelligent Pull Request Reviews
Configure AI to run preliminary code review, check edge cases and architectural standards before human review. Fewer back-and-forth cycles, higher baseline quality.
How We Work
How we ship. From audit to production.
01
Architecture & Security Audit
1–2 weeks
Deep analysis of your infrastructure: databases, APIs, IAM, CI/CD. Identifying bottlenecks and security risks before AI integration begins.
02
MVP Definition & Data Boundaries
Discovery sprint
Defining clear project scope. Configuring data isolation policies and selecting the optimal model stack for your cost/latency balance.
03
Engineering & Integration
Build phase
Writing reliable, testable code. Integrating AI microservices with your existing backend through secure, observable channels.
04
Observability & Production Rollout
Launch & handover
Setting up monitoring: token tracking, error logging, latency dashboards. Shadow deployment, load testing, and knowledge transfer to your in-house team.
Stop building brittle AI demos.
Let's build production systems.
Request a Technical Architecture Audit. Our senior engineers will analyze your system and show you how AI can be safely and scalably integrated into your backend. No marketing decks — just code, architecture, and metrics.