Not a demo, and not a proof of concept that impresses a meeting and then stalls. Production means live data, real integrations, measured acceptance criteria and people using it in their actual workflow. This page is how we do that, who does it, and what you end up owning.
Not pilots. Each of these runs on live data, inside a business process, with the numbers measured on the engagement.
A talent-visa platform: RAG drafting grounded in the client’s own document sets, case-manager workflow, SLA monitoring and a support assistant.
A lending marketplace: document authenticity, face match, liveness and behavioural signals, embedded in the live loan-origination flow rather than beside it.
Civil-aviation MRO: computer vision over maintenance packages — page classification, signature and stamp detection, cross-package consistency — shipped as a CLI and an HTTP API.
Implementation isn’t a fourth kind of project — it’s how the three things we build actually reach production.
LLM applications and RAG pipelines over your own documents, AI agents and workflow automation, document intelligence and computer vision, data platforms, and the integrations into the systems you already run.
Inference and training clusters, GPU compute, networking, storage and the platform layer — deployed, benchmarked and handed over as a running environment rather than a delivery of boxes.
Edge inference, robotics integration, digital twins and simulation — where the system meets machines, sensors and a physical process that doesn’t pause for a deployment window.
Roughly 80% of getting AI into production is data engineering, integration, governance and measurement — work that has nothing to do with the model itself. The most common blocker is data: a pilot runs on a curated slice, while production needs continuous, governed access to live data across systems.
Which is why a proof of concept optimized for a demo has to be rebuilt to scale — and the rebuild is where momentum dies. We build the first increment as a small production system, not as a demo we’ll fix later.
| Pilot | Production | |
|---|---|---|
| Data | A curated, hand-cleaned slice | Continuous, governed, live — across systems |
| Users | A few, supervised | Everyone in scope, by default |
| Wrong answers | Tolerated, watched by the team | Caught by a wired-in fallback and monitoring |
| Integration | Often mocked or manual | Connected to the real workflow and systems |
| Ownership | The project team | A named operations owner, for the system’s life |
The long version: From Blueprint to Implementation · Why AI pilots fail
Six phases, each with an explicit “done.” A production implementation typically runs three to nine months, depending on scope and the state of your data. For clients on the full adoption track, these continue from the blueprint.
Data pipelines, platform, environments, CI/CD, identity and secrets, observability, governed data access.
Built to the design in increments — the model and agent layer, integrations, workflow, guardrails and human-in-the-loop points. Never one big bang.
Tested against the acceptance criteria on a held-out set: accuracy, latency, cost per transaction, safety, red lines. Results go on the record, not into a slide.
Small but real — live traffic, monitoring on, human fallback active, wired into the actual workflow rather than run beside it.
Training, workflow redesign, support, and the review points the design specified. The phase companies most often under-resource.
Monitoring for drift and quality decay, retraining triggers, security upkeep, run-cost management, and an owner with a name.
Ten engineers work on AI solutions full time, alongside the infrastructure practice that deploys the compute underneath them. Teams are assembled to the design rather than allocated from a bench — so you deal with the same people from scoping through acceptance, and the architect who wrote the technical design stays on it until the system is accepted into production.
Owns the architecture and the technical decisions, from design through acceptance.
Owns scope, schedule and acceptance criteria, and runs the engagement end to end. Your single point of contact.
Typically two, full-cycle: model and agent development, evaluation, and taking it to production. On research-heavy work this splits into a scientist who tunes the model and an engineer who productionizes it.
The services, APIs and integrations — and the interface, where the solution needs one.
The interface is designed and approved against the acceptance criteria before it is built, not decorated afterwards. Adoption is the phase most builds fail at, and this is where that starts.
A delivery manager on larger engagements. Infrastructure and network engineers when the solution needs its own compute. Edge and robotics engineers for physical AI.
A specification written to be executable by any competent team is only worth something if you are actually free to use it that way.
Our team runs all six phases end to end. One point of accountability for the result.
Your engineers and ours in one team — typically we own the AI and integration layer, you own the domain systems and carry the knowledge forward.
You build; we review the architecture, the evaluation design and production readiness at defined gates.
Implementation ends on a signature, the same way the design did — against criteria agreed before anyone wrote code.
Not as a policy — as arithmetic. To price and schedule an implementation honestly, someone has to have settled which models and agents, what level of autonomy, which systems it integrates with, how data flows, what security applies and what “done” means. Those are also the things that move the number, along with the state of your data and how far the rollout goes. If they aren’t settled, a quote is a guess — and the gap between the guess and the reality is exactly where AI budgets double.
Have an approved specification? Bring it — we’ll read it and tell you where it’s thin before quoting anything. Don’t have one? Then that is the first project, and it is far cheaper than finding out mid-build.
Start with a blueprint →If you have a design, bring it — the review is short and it tells you more than a proposal would. If you don’t, that’s the first thing to build.
Yes, if the specification settles what a build depends on — models and agents, autonomy, integrations, data flow, security and acceptance criteria. Send it over; the review is the fastest way to find out, and it is usually short.
Then that is the first project, and it isn’t ours to skip. The AI Solution Blueprint produces the functional and technical design — one system from $30,000 in 5–6 weeks, or from $15,000 per initiative across a portfolio, and executable by any competent team, including one that isn’t us.
Yes, and sometimes that is the right call. That is why the design is written to be portable. We also take oversight-only engagements: you build, and we review the architecture, the evaluation design and the production-readiness gates.
Three to nine months for a production implementation, depending on scope. The honest driver is your data — whether it is accessible, governed and continuous, or has to be made so. The timeline is fixed in the proposal against the approved design.
You own it — source, infrastructure-as-code, prompts, evaluation sets and documentation, delivered to your accounts. Defects are covered for six months after acceptance as standard, in the contract. Beyond that, ongoing operation is a support arrangement if you want one, or your team’s, with the runbooks we hand over.
Yes, and it is a common shape — inference or training clusters, networking, storage and the platform layer, deployed and benchmarked alongside the solution. See Private AI Infrastructure.