FAILURE 01
Demo-grade evaluation
It was tested on twenty happy-path examples. Nobody measured what happens on the other ten thousand, so nobody can tell whether a change made it better or worse.
AI development consultancy
Everyone bought AI. Almost nobody shipped it.
Corporate Noose builds AI systems that make it out of the pilot and into production — agents, LLM pipelines and automation engineered for the messy reality of your business, not the conditions of a demo.
Discovery → working system → handover. Fixed scope, fixed price.
01 / The gap
The pilot works. The rollout doesn't.
Nearly every organisation now has an AI proof of concept. Far fewer have anything load-bearing. The distance between the two is not model quality — it is everything around the model.
FAILURE 02
No integration surface
The system can reason, but it can't reach the CRM, the warehouse, the ticketing queue or the permissions model. It produces suggestions nobody is able to action.
FAILURE 03
Unowned in production
No cost ceiling, no latency budget, no traces, no rollback. The first bad week burns the trust the pilot bought, and the project quietly dies.
02 / Services
What we build
Four kinds of engagement. Most clients start with the first and move down the list.
AI strategy & feasibility
A short, sharp engagement that separates the AI work worth funding from the AI work that sounds good in a board deck. We map your processes, score candidate use cases on value and tractability, and prototype the riskiest assumption before you commit a budget to it.
You leave with a ranked roadmap, honest cost modelling, and a clear list of the things we think you should not build.
Use-case mappingFeasibility spikesCost & latency modellingBuild vs buy
Custom LLM applications
End-to-end development of AI products against your own data and workflows: retrieval over internal knowledge, document extraction and classification, drafting and summarisation tools, natural-language interfaces to systems your team already uses.
Built as ordinary software — versioned, tested, observable, deployable — with the model treated as one component rather than the entire architecture.
RAG & hybrid retrievalStructured extractionEvaluation harnessesHuman-in-the-loop
Agents & workflow automation
Systems that take action, not just produce text. We build tool-using agents with the parts that make them safe to run unattended: scoped permissions, deterministic control flow where determinism belongs, spend and rate limits, audit trails, and a human approval step wherever the blast radius justifies one.
We are deliberately conservative about where agency is warranted. A well-placed state machine beats an autonomous loop more often than the market admits.
Tool-use designMCP integrationsGuardrails & approvalsAudit logging
Production hardening
For teams who already have something working and need it to be dependable. We instrument what you have, build the evaluation suite that should have existed from the start, cut token spend and latency, and put the operational scaffolding in place so your engineers can own it after we go.
Frequently the highest-return work we do, and the least glamorous.
Eval suitesObservability & tracingCost reductionRegression testing
03 / Approach
How an engagement runs
No open-ended discovery phases. Every stage produces something you could take to another supplier and continue with.
01
Scope
Week 1
We work out what success actually means in numbers before writing anything. What gets measured, what threshold makes it worth deploying, what it may not do. If we don't believe the numbers are reachable, we say so here and you have spent one week.
02
Prove
Weeks 2–3
The riskiest component gets built first, against your real data, and measured against the evaluation set we agreed in scope. Not a UI, not a slide — a number, and a judgement about whether to continue.
03
Build
Weeks 4–10
The full system, in your infrastructure, integrated with the tools your team already uses. Weekly demos against a running environment. Evaluation scores tracked per change, so quality regressions are visible the day they happen rather than the quarter after.
04
Hand over
Final two weeks
Documentation, architecture walkthroughs and pairing sessions with the engineers who inherit it. You own the code and the model relationships outright. Ongoing support is available; dependence on us is not designed in.
04 / Position
We are not selling you a transformation.
The name is a joke about what the industry is doing to itself: enormous spend, enormous claims, and a thin layer of working software underneath. We would rather deliver one boring system that runs every day than a strategy nobody can implement.
That means we will occasionally talk you out of an AI project. Sometimes the honest answer is a database query, a rules engine, or a fixed process — and those are far cheaper to run than a language model.
// what we commit to
scope fixed, written, priced up front
evals agreed before the build starts
code yours, in your repos, from day one
infra your cloud, your model accounts
lock_in none
handover included, not an upsell
// what we won't do
reject unmeasurable success criteria
reject agents without guardrails
reject AI where a query would do
Model-agnostic
We build against whichever model fits the task and the budget, and keep the swap cheap. Frontier models where reasoning is needed, small ones where it isn't.
Your infrastructure
Everything runs in your cloud accounts under your keys. No proprietary runtime, no per-seat platform fee, nothing that stops working when we do.
Small senior team
The people who scope the work are the people who build it. No layer of account management between you and the engineers.
Next step
Tell us what isn't shipping.
A 30-minute call, no deck. Describe the process you want to change and we'll tell you whether it's a good candidate for AI, roughly what it would cost, and what we'd do first. If it isn't a fit, that's a useful call too.