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Where AI earns its keep
Agents, RAG retrieval and intelligent automation woven into your workflows where they create real leverage — not bolted on for a demo. Built on your data, measured against real outcomes, and guardrailed for production.
AI woven into
your workflow
We do not sell you a chatbot and leave. We find the workflow where AI creates real leverage, build it on your data, and prove it works before it touches production.
Agents that
do the work
Autonomous agents that call your tools, follow your rules and complete real tasks — triaging tickets, drafting replies, updating records. Scoped tight, observable end to end, and stopped cold by guardrails when they should ask a human.
Answers from
your own data
Retrieval over your contracts, wikis, tickets and PDFs — so the model answers from what your company actually knows, not the open internet. Every answer is sourced and traceable, which is what makes people trust it.
A copilot inside
the tools you use
Embedded assistants in your product, CRM or internal app that draft, summarise, search and take the next step in context. Your team stays in their workflow instead of pasting into a chatbot in another tab.
Ops that run
themselves
Classify, extract, route and enrich across the messy middle of your operations — invoices, intake forms, support queues, data clean-up. The dull, error-prone steps become reliable pipelines your team stops touching.
AI you can
actually trust
Evaluation harnesses, golden datasets and groundedness checks so accuracy is a number you can watch — plus input/output guardrails, PII handling and human-in-the-loop on the calls that matter. Shipped with the safety on.
The stack behind
AI that ships
We are model-agnostic and opinionated about the parts that actually matter — retrieval, evaluation and guardrails. The model is the easy bit; the pipeline around it is what makes AI dependable in production.
Reasoning & generation
The model layer itself — drafting, summarising, classifying, reasoning. We pick per task and stay portable, so a better or cheaper model is a config change, not a rewrite.
Retrieval over your data
RAG that answers from your own documents with citations. Real chunking, embeddings and reranking on infrastructure you already run — not a black-box index you cannot inspect.
Agents & orchestration
When the AI has to plan, call tools and complete multi-step tasks. Typed tool definitions, deterministic control flow and full traces so you can see exactly what it did.
Evaluation & observability
How we know it works before and after launch. Golden datasets, automated evals, groundedness scoring and tracing so quality is a metric you can watch, not a hope.
Automation platforms
Wiring AI into the tools you already use — CRMs, inboxes, ticketing, data warehouses. Webhooks, queues and connectors that fail loudly and recover gracefully.
Safety & guardrails
For anything customer-facing or sensitive. Input/output validation, PII redaction, prompt-injection defenses and human-in-the-loop on the high-stakes calls.
A working prototype
in two weeks
We start with the workflow, not the technology. Short loops, evals from day one, and a metric you can watch — so you see AI working on your data, not on a slide.
Find the high-leverage workflow
We look at where your team loses hours and where errors hurt, then pick the one workflow AI can move the needle on. You leave with a target metric and a clear definition of "good enough to ship".
Prototype with evals from day one
We build a working prototype against your real data and a golden test set in parallel. Accuracy is a number from the first week — so we tune retrieval and prompts against evidence, not opinions.
Integrate into your stack
We wire it into the tools you actually use — your CRM, inbox, app or warehouse — with typed tool calls, auth and the guardrails on. No more copy-pasting into a chatbot in another tab.
Measure, harden & launch
PII handling, prompt-injection defenses, rate limits and human-in-the-loop on the high-stakes calls. We ship behind a metric you can watch, with tracing on every run and a kill switch you control.
Iterate as models improve
Models get better and cheaper every quarter; your evals let us swap them in safely. We tune against live results, expand to the next workflow, or hand off cleanly. No lock-in, your data stays yours.
Real leverage. Measured. No demo-ware.
Anyone can wire up a chatbot in an afternoon. The difference is making it accurate, safe and worth the spend — and proving it with evidence before it goes live.
Three ways to start
Not sure where AI pays off? Most teams begin with an AI Audit — low risk, and the fee rolls straight into the build if a use case proves its worth.
- Workflow & data readiness review
- Prioritised list of high-leverage use cases
- Working proof-of-concept on your data
- Eval plan & target accuracy metric
- Fixed estimate, credited toward the build
- Production deployment
- Ongoing optimisation
- Everything in Discovery
- Full build — agent, RAG or automation
- Integrated into your tools & data
- Eval harness & groundedness scoring
- Guardrails, PII handling & human-in-the-loop
- Launch + handover of code & docs
- 30 days post-launch tuning
- Senior AI engineers embedded with you
- Continuous delivery across use cases
- Model upgrades tracked & swapped safely
- Ongoing eval & accuracy monitoring
- Priorities you set each week
- Scale the team up or down
- No long-term contract, no lock-in
The questions
everyone asks about AI
Straight answers, no hype.
What happens to our data — does it train someone’s model?
No. We use enterprise API tiers (Anthropic, OpenAI, Azure) where your data is not used for training and is not retained beyond the request. Retrieval runs on infrastructure you control — typically your own Postgres with pgvector — so your documents never leave your environment to be indexed. We add PII redaction where the use case calls for it.
How do you stop it from hallucinating or being wrong?
Three layers. We ground answers in your data with retrieval so the model works from real sources, not memory. We measure it with an eval harness and a groundedness score, so accuracy is a number we tune against — not a hope. And we put a human in the loop on the high-stakes calls. If it cannot answer confidently from the sources, it says so instead of guessing.
Should we build this or just buy an off-the-shelf tool?
Often you should buy — and we will tell you when. Off-the-shelf wins for generic, horizontal tasks. We build when the value is in your data, your workflow or your product, where a generic tool cannot reach. The AI Audit gives you that build-vs-buy answer up front, before you commit to anything.
Which model should we use — Claude, GPT, something open-source?
It depends on the task, and we stay model-agnostic so it is never a lock-in. We benchmark candidates against your eval set on quality, latency and cost, then pick per use case. Because the evals are in place, swapping to a better or cheaper model later is a config change, not a rebuild — which matters a lot given how fast this moves.
How do we know it’s actually worth the spend — what’s the ROI?
We tie every build to a metric before we start: hours saved, response time, deflection rate, error rate. The prototype proves the lift on your real data in week one or two, and we keep watching it in production. If a use case does not earn its keep, you find out cheaply at the audit stage rather than after a long build.
Who owns the code, the prompts and the pipeline?
You do — fully. The repo, the prompts, the eval datasets and the infrastructure are yours from day one. No proprietary platform fee to keep your own AI running, no hostage source. If we part ways, any competent team can pick it up.
How fast can we see something working?
An AI Audit can usually kick off within a week. From there you have a working proof-of-concept running against your own data, with accuracy numbers, in one to two weeks — not a slide deck, the real thing you can click.
Tell us where the work piles up.
We’ll show you where AI pays off.
Bring a workflow, a pile of documents, or just a hunch. In one call we’ll find the highest-leverage use case, a rough timeline and an honest read on whether to build or buy — no obligation, no hype.
Reply within 24 hours · Senior AI engineer on the call · Really