AI implementation services for teams whose pilot never shipped.
The gap between a proof of concept and a system people use every day is not model quality. It is integration, data plumbing, evaluation, permissions, and the unglamorous work of changing how a team operates.
- A proof of concept that has been nearly ready for two quarters.
- A model that works on an export but not on the live system.
- Security or compliance review that no one planned for.
- A tool that shipped and that nobody in the target team opens.
What’s included
Integration
Connected to the systems where the work already happens, with the auth and permissions that implies.
Data pipelines
Getting the right data to the model reliably and on time — usually the longest pole in the work.
Security and compliance
Access control, data residency, and audit trails designed in rather than retrofitted under review.
Evaluation and monitoring
A quality bar that is measured continuously rather than asserted at launch.
Rollout
Staged release with a path back, so the first real users are not the whole company.
Enablement and handover
Documentation, runbooks, and your team able to operate and extend the system without us.
How we work
- Prove3–4 weeks
Production PoC
Build one use case for real — and measure it.
- One use case built production-grade
- Evaluated against a real KPI
- Evidence-based go / no-go
- Ship6–12 weeks
Build & Integrate
Put the system into production — integrated and compliant.
- Full build, integrated with your stack
- Security & compliance built in
- Live, monitored, documented
- ScaleOngoing
Scale & Enable
Expand across workflows — and level up your team.
- Rollout across teams and workflows
- Evals & monitoring in place
- Team enablement + fractional AI leadership
The last mile is integration, and it is most of the distance
A proof of concept runs against a data extract, authenticates as one person, and is judged by the team that built it. A production system reads live data, respects every permission its users have, degrades sensibly when a dependency is down, and is judged by people who did not ask for it.
Almost none of that distance is model work. It is API contracts, identity, error handling, rate limits, and the reconciliation of what a system claims to store with what it actually stores. Teams that budget for the model and not for this arrive at a demo that cannot be deployed, which is the single most common way enterprise AI fails.
Data pipelines are the long pole
The data that made the prototype work was probably assembled by hand — one clean export, deduplicated by someone who understood the edge cases. Production needs that same assembly to happen continuously, correctly, without supervision.
That means ingestion that handles a source going down, transformations that fail loudly rather than silently emitting nulls, freshness guarantees the application can rely on, and a permission model that travels with the record. Estimate this honestly at the start and the timeline holds. Discover it in week six and it does not.
Adoption is a design constraint
A system nobody uses returns nothing regardless of how well it performs. Adoption is decided by where the tool sits, how much it asks of a user before it gives anything back, and whether it fits a workflow people already have.
In practice: put the capability inside the tool the team already opens rather than beside it, make the first interaction cheap, and give people a way to see why the system produced what it did. Trust is built by explicability far more than by accuracy claims, and a staged rollout gives you the feedback to fix the workflow before it is everyone’s problem.
- We have a proof of concept already. Can you take it to production?
- Yes, and it is common work. The first step is an honest assessment of what carries forward — often the approach is sound and the data, integration, and evaluation layers need building properly.
- How long does implementation take?
- Build and Integrate runs six to twelve weeks for a validated use case. The variable is rarely the model; it is the number of systems to integrate and the state of the data.
- Do you work with our engineers or replace them?
- Alongside them. Handover is an explicit deliverable — documentation, runbooks, and enablement so your team can operate and extend the system after we leave.
- What about security review?
- It is designed in from the start rather than met at the end. Access control, data residency, and audit trails are architecture decisions, and our engineers have delivered in regulated banking environments.
- What if the proof of concept should not go to production?
- We will tell you. A Production PoC ends in an evidence-based go or no-go, and a well-argued no-go saves more than a reluctant yes.