AI systems that survive contact with production.
Most AI projects die between the demo and the first thousand real users. We build the unglamorous half — retrieval quality, evaluation harnesses, fallback paths, cost ceilings, and clean human handoff — so the intelligent half still works when it matters.
- 01retrieve.knowledge_base142ms8 chunks
- 02rerank.cross_encoder88mstop 4
- 03tools.subscription_lookup310msplan=growth
- 04compose.answer1.9s3 citations
- 05policy.pii_check22mspass
- 06deliver.reply—sent
Below 0.72 the run halts and opens a ticket with the full transcript attached.
“Transforming raw data into smart, automated workflows that scale your business 24/7.”
Where AI projects usually break
If two or more of these sound familiar, the underlying issue is usually architectural rather than a shortage of effort. That is the part we are good at.
- The prototype that impressed the board starts hallucinating on real customer data.
- Inference spend scales linearly with growth and nobody can explain the invoice.
- There is no evaluation set, so every prompt change is a coin flip in production.
- The model can answer questions but cannot actually act on your systems.
- Sensitive records leak into prompts because nothing enforces per-user access.
Five things we deliver inside this practice.
Each one is a complete piece of engineering, not a line item on a proposal.
AI Chatbots & Virtual Assistants
Support and lead-generation assistants grounded in your product, wired into your CRM, with an escalation path a human can trust.
- Intent routing with confidence thresholds
- Full-transcript handoff to a live agent
- Brand-constrained tone and refusal policy
- Deflection and CSAT instrumentation
Custom RAG & Knowledge Base Systems
Q&A over contracts, wikis, tickets, and code — with citations, freshness guarantees, and permissions enforced per user.
- Hybrid dense and keyword retrieval with reranking
- Incremental ingestion and re-indexing jobs
- Row-level access control applied at query time
- Answer citations linked to source spans
Autonomous AI Agents & Workflows
Multi-step agents that plan, call your internal tools, and know when to stop and ask a human.
- Typed tool contracts with schema validation
- Deterministic state machine around the model
- Step budgets, retries, and idempotent actions
- Full execution traces for every run
AI Document & CV Processing
Structured extraction from PDFs, resumes, invoices, and scans — validated against your schema and scored for confidence.
- Layout-aware parsing for scanned documents
- Strict output schemas with repair passes
- Confidence scoring and review queues
- Human corrections that feed back into the system
Smart Content & Research Engines
Automated research pipelines that gather, verify, and draft — and always hand back the source trail.
- Multi-source gathering with deduplication
- Claim verification before drafting
- House-style and compliance constraints
- Editor review workflow with diffing
Results from this practice
How this engagement runs.
Named phases with named outputs. You always know what is being produced and when you will see it.
- P1Week 1
Evaluation set first
Before a single prompt is written we build the answer key: a hundred real questions from your domain with expected outcomes. Every later change is measured against it.
Golden evaluation setAccuracy baselineFailure taxonomy - P2Week 1-2
Retrieval spike
We prove the system can find the right context before we ask a model to reason over it. Retrieval quality is where accuracy is won or lost.
Ingestion pipelineRecall reportChunking strategy - P3Week 2-4
Reasoning and tool layer
Agents get typed tool contracts, step budgets, and a deterministic state machine around them, so behaviour is inspectable instead of emergent.
Agent graphTool adaptersTrace viewer - P4Week 4-5
Harden and constrain
Prompt-injection defences, PII redaction, rate limits, per-tenant cost ceilings, and graceful degradation when a provider goes down.
Guardrail suiteCost ceilingsFallback routing - P5Week 5+
Ship and observe
Staged rollout behind feature flags, with dashboards your team reads daily and a runbook for when the model is wrong.
Live deploymentObservability dashboardsOperations runbook
The stack we reach for
Chosen for operational boredom, not novelty. If something newer is genuinely better for your case we will say so, and explain the maintenance cost.
What you receive
Every engagement ends with these in your accounts, whether or not we keep working together afterwards.
- Versioned prompt registry with rollback
- Reproducible evaluation suite wired into CI
- Ingestion and re-indexing jobs
- Trace-level observability per request
- Per-tenant cost ceilings and rate limits
- Handover session and written runbook
This capability, applied.
The engineering decisions and the numbers that followed them.
Questions we get asked
Yes. We deploy against self-hosted models or your cloud provider's managed endpoints when data residency demands it, and design the abstraction so you can move between providers without a rewrite.
Three layers: retrieval that returns the right evidence, prompts that require citation of that evidence, and a verification pass that refuses to answer below a confidence threshold. What cannot be grounded gets escalated to a human rather than invented.
Model routing sends easy requests to small models, caching removes repeated work, and hard per-tenant token ceilings cap the worst case. You get a cost dashboard before launch, not after the first invoice.
Let us talk about ai & intelligent systems.
Two ways to start, both of them short. Bring the problem, not a specification — the first useful thing we do is tell you what we would build and roughly what it costs.
Book an architecture call
Fifteen minutes, no deck. We map your problem to an approach and tell you what a realistic scope and budget look like.
- A specific technical recommendation
- A budget band you can plan against
- An honest answer if we are the wrong fit
Send a written brief
Prefer to write it down? Email us the shape of the problem and we will reply with a first take, usually under 12 hours.
- Goes straight to an engineer, not a sales inbox
- We reply with an approach, not a brochure
- Attach anything: repos, docs, screenshots