Specialist AI Engineering Studio

We integrate AI into your B2B backend without rewriting what works.

Every engagement starts with an architecture audit — risk mapped before code is written. AI ships behind feature flags, connects to your existing auth, and rolls back cleanly. Your current product keeps running.

Request Architecture Audit See where generic AI breaks
The Problem

Demo AI vs. Production Systems

Most agencies sell Python scripts on top of OpenAI API. It works in a sandbox. It breaks when it meets real B2B production.

Your AI feature is live. Which column describes what happens when it fails at 3am?

Architecture

Typical AI Agency

Monolithic CallsHard-Coded APIsNo Fallback

Production-Grade AI

Circuit BreakersProvider RoutingTenant Isolation
IntegrationShallow→Deep
Failure ModeCascading→Isolated
Failure blast radiusFull product→AI only
Observability

Typical AI Agency

No TracingBlack BoxSilent Errors

Production-Grade AI

Distributed TracesToken MetricsAlert Policies
Debug TimeHours→Minutes
Incident MTTR4–8 h→15 min
Monthly eng burn~18 h debug→~45 min
Performance

Typical AI Agency

No CachingUnbounded CallsHigh Latency

Production-Grade AI

Semantic CacheRate LimitingSub-200ms
Avg Latency2,400 ms→180 ms
Throughput12 req/s→840 req/s
LLM spend at scaleGrows linearly→Capped by cache
Privacy & Data

Typical AI Agency

Shared ContextNo Data ScopeRaw Model Calls

Production-Grade AI

RBAC EnforcedTenant IsolationData Masking
Data LeakageHigh Risk→Zero
Isolation ModelNone→Per-tenant
Tenant data leakContract breach→Impossible
Security

Typical AI Agency

API Keys in CodeNo Audit LogOpen Endpoints

Production-Grade AI

Secrets ManagerAudit TrailmTLS + IAM
Access ControlNone→RBAC + IAM
Audit Coverage0%→100%
Credential exposureGit history→Vault-managed
Who we work with

We work with patterns, not industries

Multi-Tenant SaaS

Your customers can't see each other's data — ever

RBAC + per-tenant RAG

Legacy Backend, New AI

Java monolith from 2012 still handles all transactions

Strangler Fig + circuit breakers

Document-Heavy SaaS

Value lives in PDFs, contracts, and case histories

Enterprise RAG + semantic cache

POC Stuck in Staging

Demo passes. Engineering won't sign off for production

Production hardening + feature flags

See how we work with each →

What we build

What we engineer for your product

Enterprise RAG & AI Search

Your users ask questions. The AI answers from your knowledge base — returning only the documents that user is authorized to read.

RBAC filtering happens at the vector retrieval layer, not after — unauthorized documents never enter the context window. Semantic caching de-duplicates repeated query patterns without re-hitting the LLM. Each tenant's embedding index is isolated; a retrieval error for one customer can't surface another's data.

Typical result: p99 < 180ms at steady state, cache hit rate > 60%

Best for: Multi-tenant B2B SaaS with role-based document access and compliance requirements

Legacy Backend Modernization

The AI layer connects to your 10-year-old backend through an adapter — your monolith never needs to know it's there.

Strangler Fig pattern: new AI capabilities live in a sidecar service that proxies through your existing auth boundary, with no schema changes to your core database. Circuit breakers at every LLM integration point — model outages stay in the AI layer and never cascade into your core transaction paths.

Typical result: AI features in production in 6–8 weeks without touching the core codebase

Best for: B2B SaaS on Java, Rails, or PHP with enterprise customers who can't absorb a rewrite

In-Product Copilots

Your users describe what they want done, and the copilot calls your internal APIs to do it — scoped to exactly what their role allows.

Tool-calling agents with hard API guardrails: the copilot can only trigger actions permitted by the current user's role. Deployed behind a feature flag with a kill switch — your existing product is never affected by a model regression.

Typical result: 35–40% reduction in support tickets for automated workflows

Best for: SaaS products where users repeat the same multi-step workflows daily

Document Pipeline Automation

Your team reviews only the exceptions — the AI handles routing, extraction, and triage on the other 90%.

Event-driven pipelines with explicit human-in-the-loop checkpoints for low-confidence decisions — nothing gets silently skipped. Per-document token cost tracked in production so you see exactly what each document type costs at scale. Dead-letter queues and retry logic ensure a model timeout never drops a document.

Typical result: 80–90% straight-through processing, cost under $0.01/document

Best for: Operations teams processing 1,000+ documents/day — contracts, tickets, invoices, intake forms

Engineering velocity

Integrate Coding Agents into your SDLC

In 2026, your AI-first competitors are shipping with teams 30–40% smaller. We bring Claude Code and Cursor into your CI/CD pipeline with the governance controls a regulated company actually needs — access scoping, audit logs, and code quality gates your security team can sign off on.

Scoped model access with per-developer audit logs. We configure Claude Code or Cursor with your repository's access policy: agents see only the directories and services relevant to their task. API keys for model calls go through your secrets manager — never in shell history or dotfiles. Every model invocation is logged: who ran it, what prompt, what context was included. Risk addressed: IP leakage — your proprietary codebase and customer data don't leave your security perimeter.
AI-generated tests in CI before any human sees the PR. On each push, a testing agent generates unit tests for changed functions, runs them, and posts a coverage diff before human review begins. Tests are generated from the actual code and your existing test conventions — not boilerplate. Nothing merges with untested logic. Risk addressed: Code quality degradation — AI-assisted developers move fast but miss edge cases; automated coverage gates catch what they skip.
Automated first-pass review against your architecture standards. Before a human reviewer opens the PR, an agent checks against your team's explicit rules: naming conventions, prohibited patterns, security anti-patterns, dependency policy. It comments inline and blocks merge on defined high-severity findings. Your senior engineers spend review time on architecture and intent — not style enforcement. Risk addressed: Audit & compliance — regulated environments need a documented review step; every AI-generated comment is timestamped and traceable.
→ Talk to us about SDLC integration
How we work

How we ship. From audit to production

PHASE_01

Architecture & Security Audit

1–2 weeks · fixed price · written report

Deep analysis of your infrastructure: databases, APIs, IAM, CI/CD. Every integration risk documented, every data boundary mapped, LLM architecture recommendation with alternatives. DeliverableWritten audit report you own — useful whether you hire us to build or not. Done when: your CTO has a document they can take into a security review.
PHASE_02

MVP Definition & Data Boundaries

1 week · signed-off scope document

Tenant data model, LLM provider selection, latency and cost SLAs, integration acceptance criteria — all agreed in writing before a line of AI code is written. DeliverableSigned scope document: what we build, what we don't, and exactly what "done" looks like. Done when: both teams agree on acceptance criteria before engineering starts.
PHASE_03

Engineering & Integration

4–8 weeks · behind feature flag

Semantic cache, RBAC enforcement, LLM fallback routing — built and tested behind a feature flag. Full test suite mirrors production data shapes and load patterns. Rollback is one config change. DeliverableWorking integration in your staging environment, test suite, documented rollback procedure. Done when: your QA team runs the Phase 2 acceptance criteria and they all pass in staging.
PHASE_04

Observability & Rollout

1–2 weeks · canary → production

Canary rollout with automatic rollback triggers. Dashboards cover latency, cost per request, cache hit rate, and error rate. Handover runbook written for your on-call team. DeliverableProduction-live AI feature, monitoring dashboards, runbook — your team owns it from day one. Done when: your SRE can explain what to page on and how to roll back without calling us.

Total: from kickoff to production — typically 7–13 weeks, depending on integration scope and your team's availability for review cycles.

What you actually get

Architectural targets,
not slide-deck promises.

Systems we build typically deliver the following. Each characteristic is engineered in as a first-class requirement — not bolted on after launch.

<200ms

p99 AI response latency

Fits inside existing UX latency budgets — no added spinners, no degraded experience for end users.

Semantic cache absorbs repeated queries before they reach the LLM; async call patterns keep the model call off the critical request path.

40–70%

LLM token cost reduction

Spend scales sub-linearly as usage grows — each cache hit costs near-zero versus a live model call.

Embedding-similarity cache normalises paraphrase variants; cosine similarity > 0.92 bypasses the LLM entirely before the request leaves your infrastructure.

4–8 wks

Kickoff to production

Fixed scope means predictable delivery — no open-ended retainer creep, no scope ambiguity after kickoff.

Architecture audit scopes every task before code is written; feature flags enable incremental ship, not a big-bang launch with a single rollback point.

3 modes

Graceful degradation on LLM failure

Provider outage or rate limit becomes a handled edge case, not an on-call incident.

Circuit breakers at every outbound call with three failure paths: serve cached response, queue for async retry, or return a typed degraded result — no exception propagation to the user.

100%

AI interaction audit coverage

Every AI interaction is attributable — who triggered it, what was retrieved, and what the model returned.

Structured log per call: tenant ID, user ID, prompt hash, retrieved document IDs, response token count — piped to your existing log sink from day one.

3+

LLM providers, zero lock-in

No single vendor's pricing, rate limits, or availability controls your production system.

Provider abstraction layer normalises OpenAI, Anthropic, and Mistral APIs — swap or weight providers with one config change, no downstream code changes required.

FAQ

Questions we get on
every discovery call.

Direct answers. If there's a caveat, we name it.

Scoping depends on what's already in place. A well-structured backend with a single integration point might be 6–8 weeks fixed. A multi-tenant system with compliance requirements typically runs 10–14 weeks. We fix the price after the audit — not before — because scope defined without seeing the system is guesswork. If the audit surfaces more complexity than expected, we tell you before we're halfway through.
No. We work inside your infrastructure — we don't proxy your LLM calls, store your vectors, or receive your data. We write the code; you run it. LLM API calls go directly from your infrastructure to your chosen provider. If GDPR or HIPAA compliance requires a DPA, we identify which providers offer it and configure correctly. Your data boundary stays yours.
You own everything. All code written during an engagement is transferred at handover — no license dependencies, no retained IP, no subscription to keep it running. We don't reuse client-specific implementations across engagements. The patterns we apply are industry-standard; the implementation is yours, with a runbook your team can follow independently.
Two principals means every decision is made by the people writing the code — no handoffs, no junior work on critical paths. The tradeoff is capacity: we run 3–4 clients at a time, not 30. If you need a 15-person delivery factory, we're not the right fit. If you need two engineers with 18+ years of production backend experience owning every architecture call — that's exactly what we are.

See all questions →

Free architecture review

Your backend is production-grade. Your AI integration should be too.

Book a free 60-minute architecture review. No NDA required.

  • We look at your current system together and identify the top 3 integration risks specific to your stack
  • You get our honest read on feasibility and realistic timeline — no optimistic estimates
  • We explain exactly what a paid audit would cover and what it costs — no surprises

What is your biggest AI challenge right now?

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