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AI that serves production, not slides.

LLM-native architectures, tailored RAG, domain-specific copilots, agents that do the real work — built for SMEs and scale-ups without an internal ML team. We treat LLMs as any other system component: measurable, reproducible, observable.

Engagement8 — 20 weeks

Team1 — 2 senior + AI specialist

OutputAI system in production

DisciplineXP + Extreme Contracts + Evals

01 · The premise

If an agent in production can't be debugged like a microservice, it isn't in production. It's a demo that lives on a server.

Generative AI has become a system primitive. It's no longer a separate section of the architecture — it's a library you manage like you manage Postgres: with monitoring, with SLOs, with rollback, with governance.

Our approach applies XP to the AI domain: small prompt releases, eval suites as tests, continuous refactor of the prompt catalog, continuous integration of evaluations in CI.

And we apply Extreme Contracts: every AI capability has declared pre-conditions (input shape, data safety), verifiable post-conditions (eval gates, latency budget, accuracy floor) and explicit fallbacks for when the model fails.

02 · What we deliver

What we deliver.

/01

LLM-native architecture

Provider strategy, routing, caching, rate limiting, observability, cost monitoring. No lock-in to the vendor of the day.

/02

Versioned eval suite

Tests executable in CI for every critical prompt. Accuracy, latency, cost, safety — four axes, thresholds signed by the client.

/03

RAG with citations

Retrieval + generation + source citation. No untraceable answers. No hallucination without alert.

/04

Agent runtime

Observable tool-use loop, with tracing, execution sandbox, fallback rules. An agent without guardrails is an incident.

/05

Data governance and security

PII handling, prompt injection defenses, data retention policy. GDPR-by-design, not bolt-on.

03 · XP in action

How we operate.

XP / Eval-Driven Dev

Evals are our tests.

For every critical prompt, an eval set. For every change, a run. For every regression, an alert.

XP / Pair Prompting

Prompts are written in pairs.

An unreviewed prompt is unreviewed code. Holds for whoever writes them with us.

Contracts / Fallback

For every AI capability, a fallback.

Model down? Rate limit? Low-confidence answer? Every case has an explicit strategy. We don't leave the user stranded.

Contracts / Data Sovereignty

Client data stays the client's.

No silent fine-tuning. No data leak to unauthorized providers. Documented permissions, audit possible.

04 · The contract

Pre-conditions, post-conditions, invariants.

Every engagement has explicit pre-conditions, measurable post-conditions, and invariants we never violate. You know what we need at the start, what comes out at the end, and what we don't negotiate in the middle.

Pre-conditions / what we need from you

Validated use case: a real end user who will use the system, not a CMO experiment.
Access to domain data (with privacy/legal clearance) or a representative dataset.
Declared inference budget: needed to size the architecture.
Agreed error tolerance: what happens when the model is wrong? How much error is acceptable?

Post-conditions / what we guarantee

AI system in production with eval gate in CI: no deploy without eval pass.
Live dashboards for accuracy, latency, cost, safety.
Operational runbook: how to handle performance drift, cost escalation, safety incidents.
Prompt + eval + tooling stack versioned in the client's repository.

05 · When it works

Right fit, wrong fit.

YESRight fit if…

You have a real problem where AI is the simplest solution, not the coolest.
You have patience for eval discipline — AI without evals is theater.
You're willing to measure inference costs and say no to features that don't pay back.
You want a system your team can extend, not a vendor black-box.

NOWrong fit if…

You want to "add AI" without a specific use case, because the board asked.
You're looking for someone to sign off on an autonomous agent executing critical actions without human supervision, from day one.
You don't want to put PII and sensitive data under governance — serious AI starts from data discipline.

/start

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