We work with patterns,
not industries
We don't specialise in healthcare, fintech, or legaltech. We specialise in the engineering problems that keep appearing across all of them: tenant isolation, legacy integration, document retrieval at scale, and getting AI from a working prototype into a production system that your SRE team can own.
When we evaluate whether an engagement is right for us, we're not asking what industry you're in. We're asking: does your system have real tenants with data boundaries? Do you have a legacy backend that can't be rewritten? Are there compliance or audit requirements that generic AI tooling ignores? The companies below have found themselves in one of these situations — and that's the lens we use to decide if we can help.
Multi-Tenant SaaS
You run a B2B SaaS product where multiple paying customers — each with their own users, data, and permissions — share the same infrastructure. Tenant isolation isn't a nice-to-have; it's what your sales team promises in every enterprise deal. You have an existing auth system, probably with RBAC, and growing pressure to ship AI search or copilot features without breaking the tenant boundary.
- LLM context windows have no native tenant isolation — without filtering at the retrieval layer, documents from Tenant A surface in Tenant B's responses
- Embedding pipelines that don't segment per tenant create shared vector spaces where isolation is statistical, not guaranteed
- Session context accumulates across requests — a user from one tenant sees answers shaped by another tenant's prior queries
- Compliance teams block launch because nobody can demonstrate what data was used to generate a given response
We implement RBAC-filtered retrieval where tenant context is applied at the vector DB query layer — not as a post-processing filter. Every embedding index is namespaced per tenant. Every retrieval operation passes through your existing auth boundary before a document enters the LLM context window. Retrieval is auditable: every query logs which tenant, which user, and which documents were included.
Per-tenant namespace in your vector store (Qdrant, Weaviate, or pgvector). RBAC pre-filter on every vector query — documents failing the permission check never reach similarity ranking. Semantic cache keyed per tenant + user role, so cache hits can't leak context across boundaries. LLM calls carry only the filtered, scoped context window. Full audit log: every retrieval records tenant ID, user ID, query, and documents included — readable by your compliance team.
Not for you if your product doesn't have B2B multi-tenancy. Single-tenant or consumer apps have a simpler isolation model and don't need this pattern — it would add complexity for no benefit.
Legacy Backend, New AI
Your core product runs on a backend that predates modern AI infrastructure by years — possibly decades. It works. Customers depend on it. Rewriting it isn't on the table. But your roadmap requires AI features, and the architecture wasn't designed for non-deterministic external calls, token budgets, or LLM provider outages. Engineering is understandably cautious about touching the core.
- Direct LLM calls from the monolith mean a provider outage propagates into your core transaction paths
- Adding AI models as synchronous dependencies breaks latency SLAs that the existing backend has met for years
- No circuit breaker means a degraded LLM response — timeout, hallucination, refusal — lands directly in user-facing logic with no isolation
- Database schemas designed for structured data can't accommodate unstructured LLM context without significant migration risk
We introduce AI capabilities as a sidecar service that proxies through your existing auth and permission boundary. The monolith never calls the LLM directly — it calls a well-typed internal API that handles the AI layer, circuit breaking, fallback routing, and observability. Your existing code sees a service call it can understand; the AI complexity stays in a contained surface area that your team can evolve independently.
Strangler Fig: new AI endpoints live in a dedicated service — matched to your existing stack language where possible — that reads from your existing DB and validates against your auth token, but never writes back without going through your core transaction layer. Circuit breakers at every outbound LLM call with three failure modes: degrade gracefully (return cached or templated response), queue for async retry, or fail fast with a clean typed error — no cascade. Distributed tracing from the AI service into your existing APM for full on-call visibility. Feature flags on every AI surface — rollback is one config change, no deploy required.
Not for you if you're building greenfield. You don't need the Strangler Fig pattern when you can design an AI-aware architecture from the start — this pattern exists specifically to protect systems that can't be redesigned.
Document-Heavy SaaS
Your product's core value is locked inside unstructured text — contracts, case files, technical documentation, research reports, compliance records. Users need to find, extract, and act on information buried across hundreds or thousands of documents. Keyword search hasn't been good enough; you're looking at AI as the answer, but the documents contain sensitive data and retrieval quality is non-negotiable.
- Naive RAG over a flat document corpus returns semantically similar passages but ignores access control — any user can retrieve any document
- Chunking strategies that ignore document structure (headers, sections, clauses) produce incoherent retrieval results for long-form content
- Without semantic caching, every user query hits the LLM — at scale, token costs grow linearly with usage and make the feature economically unviable
- Retrieval quality degrades silently — hallucinated citations look confident, and without monitoring you won't know until a customer reports it
We build a retrieval pipeline that understands your document structure — chunking by semantic section, not by raw token count. RBAC filtering is scoped to document-level permissions. A semantic cache normalises paraphrase variants of the same query, cutting LLM calls on repeated patterns by 60%+. Retrieval quality is tracked in production: cosine similarity distribution, citation accuracy sampling, and automatic fallback to keyword search when embedding retrieval confidence is below threshold.
Ingestion pipeline: parsing → section-aware chunking (respects headers, clauses, semantic boundaries) → embedding → indexing with metadata (document ID, owner, access policy, section type). Query layer: access policy applied as a pre-filter, semantic similarity ranking on the filtered set only. Semantic cache: query normalisation → embedding comparison → cache hit if cosine similarity > 0.92 → LLM bypassed entirely. Quality monitoring: per-query logging of retrieved documents and cosine scores; periodic sampling for citation accuracy; cache hit rate and cost-per-query tracked as first-class production metrics — visible in your team's dashboards.
Not for you if your data is fully structured — database records, JSON APIs, tabular data. RAG is the wrong pattern when your data is already queryable with standard tools. The retrieval problem only appears when meaning is buried in prose.
POC Stuck in Staging
You built an AI integration that demos well. The prototype convinced your leadership. Now engineering is blocking the production launch — security review raised concerns, the latency numbers don't meet SLA, or there's no rollback plan if the model behaves unexpectedly under real load. You have something that works in a sandbox; you need someone to get it across the production line.
- The prototype called the LLM directly with no fallback — any provider outage or rate limit becomes a user-facing error with no graceful degradation
- Latency was acceptable in a demo (2–4 seconds) but is unacceptable at production UX standards for synchronous features
- No feature flag means shipping equals full exposure — there's no gradual rollout, no rollback without a full redeploy
- The security team has no way to answer "what data did the AI see for this response?" — audit trail is absent from the prototype
We take your existing POC and harden it for production: circuit breakers, latency optimisation through semantic caching and async patterns, feature flag deployment for gradual rollout, and a full audit trail for every LLM interaction. We define acceptance criteria with your security and engineering teams before implementation starts — so the production launch is a gate you know how to pass, not a moving target.
POC audit: we review your existing implementation against a production checklist — failure modes, data boundary, latency profile, secret management, observability gaps. Hardening plan: replace synchronous LLM calls that block the request thread with async patterns where UX allows; add semantic cache for repeated query patterns; wrap every outbound call with a circuit breaker returning a typed degraded response on failure. Feature flag rollout: 1% → 10% → 50% → 100%, with automatic rollback trigger if error rate exceeds agreed threshold. Acceptance criteria agreed in writing before implementation: p99 latency ceiling, error rate ceiling, cache hit rate floor, audit coverage — all measurable, all owned by your team after handover.
Not for you if your POC was built on a fully managed AI platform (Copilot Studio, Vertex AI Agent Builder, etc.) with no custom integration layer. Those platforms have their own hardening paths and production controls — this engagement pattern is for custom-built integrations that need production engineering applied to them.
Not sure if you fit?
If your situation doesn't match any of these exactly, describe it. We'll tell you honestly whether we can help — and if not, why not.
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