Readiness score
An honest read on where you stand against each requirement, not a green light by default.
Four ways to work with us
Each mode is deliberately scoped. Find where you are; the next step follows.
AI Readiness
Before anyone writes code, we work out whether you are actually ready to build. If the basics are not there, building is premature, and we will say so plainly. You leave knowing whether to invest now, what to fix first, and what is really standing in the way.
What we look for
The five things that decide whether AI adoption is realistic or premature.
- Whether the data is there: available, clean enough, and something you are allowed to use and trace.
- Whether you would know it is working: can "good" be defined and checked for this use case, or is it a matter of opinion?
- Your security and governance footing: identity, secrets, approvals, and the controls a model would inherit.
- How well it fits: the systems an AI or agent would touch, and how much independence the workflow can safely give it.
- Whether the work suits AI at all: does the process tolerate answers that are usually, but not always, right?
What we deliver
Four things you can put in front of a board, a regulator, or your own engineers.
Gap analysis
The distance between where you are and where you need to be, with the severity of each gap.
Risk inventory
The things that could block, slow, or quietly undermine a rollout, each with an owner.
Prioritized roadmap
What to do first, ordered by impact, dependency, and how hard it is to fix.
Metrics that close this engagement
We agree these at kickoff; the work is done when they are met.
- Readiness coverage: how much of the picture is genuinely solid, scored across the dimensions above.
- Critical-gap ownership: every serious gap has a name against it, a plan, and a date people have agreed to.
- Roadmap acceptance: the people who have to act on the roadmap have signed up to its order and dependencies.
- Time to decision: how long it takes to reach a go, no-go, or fix-first call that leadership will stand behind.
AI Foundation
For teams that know AI is coming but do not yet have anything underneath it. We build the groundwork, document it end to end, and hand you a baseline you own, with a clear route to building real things on top.
What we look for
The starting conditions that shape what the baseline needs to be.
- Where to start: which workflows the foundation has to support first, and what success looks like for them.
- Your data and how it is reached: where it lives, who governs it, and how the system will retrieve it.
- Your platform today: cloud, identity, networking, and where AI workloads will actually run.
- Who takes it forward: the team that will run this after we leave, and how deep their ownership goes.
- The boundaries: the regulatory, contractual, and policy lines the baseline has to respect.
What we deliver
A baseline across the concerns that actually decide whether production AI holds up.
A way to measure quality
A practical way to tell whether a change made things better or worse, run on every release.
Retrieval and grounding
Indexing, retrieval, and grounding set up properly and actually measured, not taken on faith.
Guardrails and safety
Input and output filtering and prompt-injection defenses, in place from day one rather than bolted on later.
Observability and tracing
The ability to follow a single request through the system, there from the start.
Governance and compliance
Access, data handling, approvals, and audit, enforced in the system rather than written in a doc.
Model and prompt management
Models and prompts kept under version control, so behavior is stable and changes are on purpose.
Metrics that close this engagement
Agreed up front, met before we call it done.
- Foundation coverage: how much of the baseline is built to a standard someone could actually review.
- Time to a gated release: how long until a change can reach production through proper checks, not by hand.
- Handover completeness: how much of the baseline (runbooks, dashboards, infrastructure, checks) your team has accepted as theirs.
- Production readiness: how much of the readiness checklist the baseline actually passes at signoff.
AI Advisory
For a team that has started, or is about to, but has not built for production before. Senior engineers in the room mean fewer wrong turns: the right calls get made sooner, and the wrong ones get caught before your users feel them.
What we look for
The system, the team, and the decisions in flight that decide whether you reach production cleanly.
- How the system is put together: where the pieces fit, where they are too tightly coupled, and what will not scale.
- What you actually know about quality today: what is measured, what is not, and how grounded the answers really are.
- Whether you can see in: can a single request be reconstructed across retrieval, prompt, and generation?
- How it holds up to misuse: what happens when untrusted input arrives, and whether the system stays in bounds.
- What is standing between you and a safe launch, ranked by how much it matters.
What we deliver
Reviewed decisions, written plans, and a bar your team can hold to after we are gone.
Quality and grounding review
A concrete plan for what to measure, how, and how to keep answers tied to real sources.
Guardrails and safety review
Where untrusted input reaches the model, and the defenses that should be standing there.
Observability and tracing plan
What to instrument, so production behavior is something you can investigate, not just watch.
Production-readiness criteria
A written bar for launch your team can hold itself to once we are gone.
Metrics that close this engagement
Set at kickoff; the engagement closes when they land.
- Decisions closed: the architecture and scoping calls settled during the engagement, each with the reasoning written down.
- Risk-register reduction: open production risks going down in number and severity against the list we start with.
- Iteration time: how long, on a typical change, from proposed to shipped on the AI surface.
- Readiness at launch: how much of the production-readiness bar is met before each release.
AI Build
Two ways in. Augmentation: senior engineers join your team to accelerate the work and leave capability behind. Outsourced: we plan it, build it, ship it, and hand it over. Either way you end with a system in production that your team owns, not a black box you are afraid to touch.
What we look for
The scope, constraints, and operating reality that decide how we plan and where the risk sits.
- What "done" means: what is being built, what counts as finished, and what is explicitly out of scope.
- What it has to talk to: the systems the build will touch, and the contracts between them.
- The data: where it comes from, how sensitive it is, and who is allowed to see it.
- What is already in place to tell if it is working, and what we will need to add to ship safely.
- Who runs it afterward: the rotation, the targets, and the operating model after handover.
- The hard limits: the regulatory, latency, cost, and availability bounds it has to hold under load.
What we deliver
A working system, plus the evidence and operating model your team needs to run it.
Shipped production system
The thing itself, live behind the right gates, taking real traffic with someone on call.
Quality and safety checks
The checks that decide whether a release is good enough, run on every change and tracked over time.
Guardrails in place
Input and output controls and prompt-injection defenses, tried against someone actively attacking them.
Observability and tracing
Per-request traces across retrieval, prompt, generation, and any tools the system calls.
Delivery pipeline
A repeatable path from code to production, with the checks, approvals, and a way back out built in.
Ownership transfer
Code, infrastructure, runbooks, and the checks, handed over and explicitly accepted by your team.
Metrics that close this engagement
Agreed up front; the build is not done until they are true.
- Production sign-off: acceptance against the readiness bar we set at scoping, with no critical items left open.
- Quality at launch: how much margin the system has on quality and safety, measured at launch and watched on every release.
- Incident response readiness: runbooks written, on-call set up, and at least one rehearsal actually run.
- Ownership transfer: how much of the system your team has formally taken over.
Not sure which mode fits?
Start with a call. We'll tell you honestly whether you're ready to build, or what to do first.