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Services · Skills, context & loops

How we make AI agents reliable, not just fast.

An orchestra doesn’t sound good because a hundred musicians play loudly. It sounds good because everyone has the same score, knows their part and follows the conductor. Context, skills, loops and graphs are the score.

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A conductor leading an orchestra with a smile — Photo: Alex Cooper / Unsplash

Context engineering

Give the agent the new-hire binder, every time.

An AI agent is a brilliant new hire with no memory of yesterday. Most AI projects fail here: the model guesses, because nobody told it what matters.

Project memory. What is in flight, what was decided and why, written where every agent reads it before starting.
Lessons. Every mistake that reached a customer becomes a rule, so it happens once.
Retrieval. The right documents, code and data pulled in for each task, not the whole haystack.
Guardrails. The rules that must never break, stated plainly and checked by tests.

Hand a talented chef your restaurant’s recipe binder and they’ll cook your menu. Hand them nothing and they’ll cook theirs.

A small team laughing together around a laptop — Photo: Vitaly Gariev / Unsplash

Skills

Recipe cards for the work that repeats.

A skill is a written procedure an agent follows the same way every time: how to review code against your security rules, how to release, how to update the help pages when a screen changes.

Consistent. The tenth release follows the same checklist as the first.
Teachable. Improve a skill once and every future run gets better.
Auditable. You can read exactly what the agent was told to do.

Review

Security and architecture checks against your own rules.

Release

Test, deploy to staging, verify, then production.

Docs

Help pages and runbooks updated with every change.

Research

Find the root cause before anything is planned or fixed.

Loop engineering

Plan, act, check, learn, and know when to stop.

Agents work in loops. A well-engineered loop has a clear goal, a budget, checks that prove the work is right, and a rule for when to stop and ask a person.

Checks, not vibes. “Done” means the tests pass and the result was seen working, not that the agent says it’s finished.
Budgets. Time, tokens and money capped per loop, so nothing runs away overnight.
Stop rules. When an agent is stuck it asks, rather than guessing louder.

An agent loop with two exits: done (checks pass, a person reviews) or stuck (stop and ask).

Graph engineering

Many agents, one relay race.

Bigger jobs are a graph: specialised agents connected by hand-offs, some running in parallel, with review steps and people at the points that matter.

Parallel where possible. Research and data prep run at the same time.
Review built in. A reviewer agent sends failing work back automatically.
People at the gates. Nothing reaches customers without a person’s approval.

A relay team doesn’t ask one runner to run four laps. Each runner is fresh for their leg, and the baton pass is practised until it never drops.

Work fans out, joins, passes review, and waits for a person before it ships.

Evaluation & observability

You can see what the AI did, and what it cost.

Quality you can measure

Test sets for the AI features themselves: accuracy, tone, refusals and the cases that matter to your customers.

Cost to the cent

Every AI call metered by feature and customer, so the bill is never a surprise.

Drift and failure alerts

Know when answers get worse or slower before your customers notice.

Everything we do, the AI-first way.

A designer sketching mobile app screens on paper — Photo: Amélie Mourichon / Unsplash

Prototypes in days

From napkin sketch to a clickable prototype you can put in front of customers this week.

A skateboarder launching into a blue sky — Photo: Oleksandr Kurchev / Unsplash

MVPs in weeks

A real, launchable first version in weeks, not the six-month surprise.

A pit crew changing tyres on a race car in seconds — Photo: Bob Kozel / Unsplash

AI-native teams

Two or three senior builders and a swarm of AI agents, instead of a ten-person org chart.

A chef plating a dish with precise tweezers — Photo: Sebastian Coman Photography / Unsplash

How we work

The process: spec first, prototype early, test everything, and ship in any language.

A slice of rainbow layer cake showing every layer — Photo: Anirban Sengupta / Unsplash

AI applications

Assistants, agents and search over your own data, built on a stack that holds up in production.

Want AI agents you can actually trust with your product?

Tell us what you want built. You’ll hear back within one business day with questions, a rough plan and an honest view of whether AI-first is the right fit.

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