Turn stalled AI prototypes
or fresh ideas into production systems.
AI-native delivery, led by senior AI architects.
For CTOs, Heads of Data, and Innovation Leads who need owned AI systems, not another demo.
- 01Architects design the system.
- 02AI agents accelerate execution.
- 03Every PR human-reviewed. Senior architects sign off at gates.
With a senior AI architect. No commitment. We'll tell you honestly if AI is the right answer.
Five technologies we ship
into production.
We architect, validate, and deliver across five AI stacks. Click any one for its use cases, model choices, and architecture pattern.
What "AI-native delivery"
actually means.
Three legs explain the operating model. The industry data confirms it. The terminal shows it running.
- 01Senior AI architects design the system before any agent writes a line.
- 02AI agents do most of the typing. Manual coding is typically under 20%.
- 03Every PR human-reviewed. Senior architects sign off at every gate.
Architecture, code generation, gate failure, architect override, gate pass.
AI-native delivery
compresses the journey.
Hover a phase to see what AI-native delivery changes, and where it doesn't. Feasibility and Discovery stay the same length because both sides need humans; Validation, Build, and Scale compress because typing is the bottleneck and agents handle most of it.
Hand-written code, sequential reviews, end-of-sprint feedback.
Agents run concept tests in parallel on sample real data in isolated environments; each gate catches failure early instead of at sprint end.
Agents run concept tests in parallel on sample real data in isolated environments; each gate catches failure early instead of at sprint end.
Senior architects.
Specialist delivery team.
You meet a senior AI architect first. Delivery is shaped by AI architects and includes a BA and PM on every engagement; ML and data engineers, software engineers (FE / BE), DevOps, and QA come on per phase as the build requires.
- 1–2AI ArchitectsLead the engagement, sign off at gates
- 1–3AI / ML EngineersSized to the model + data work
- 1Data EngineerPipelines, identity, feature store
- 0.5–1DevOps / MLOpsDeployment, monitoring, retraining
- as neededSE + QAFE / BE engineers, QA per build phase
BA + PM are always on, every engagement. They run project shape and stakeholder alignment from day one.
Scoping a PoC?
Decide in 4 to 8 weeks.
For fundraising, stakeholder buy-in, technical go / no-go, or simply: will this AI approach hold up on data we can actually show it. Same architectural standard as a production engagement, sized to a single decision, with kill criteria named before any build.
- 01Scope, Work with our architects to design a focused PoC that will surface the insights you need to decide on production, with clear kill criteria and a named decision owner.
- 02Build and validate, Our architects and engineers build the PoC, working alongside your team and using your data, and validate it against the kill criteria you set in the scoping phase. Real data, isolated environment, no production integration.
- 03Decide, Present the findings to your stakeholders, make a go / no-go decision on production, and get a written read from our architects on the plan and results to inform next steps, whether with us or your internal team.
- 04Next steps, The PoC approach, architecture, and code are all built with scalability in mind and can be directly lifted into a production solution with us or your internal team. We help you understand the tradeoffs and decision points to make the best choice for your org.
Beyond the PoC: the architecture and code are built to lift directly into production with your team or with us, so the investment carries forward into the next phase rather than being thrown away.
What a real delivery
looks like, end to end.
Three across financial services, e-commerce, and healthcare. Click any one for the full engagement, phases, gates, the redirect mid-Validation, and the metric the project was measured against.
Twelve more across e-commerce, ed-tech, healthcare, and private AI.
All case studies
Built by AI architects, not salespeople.
For the gap after the demo.
RemiLink was founded after years building production ML systems and watching promising demos fail when they met real data, integrations, evaluation, ownership, and handoff.
That is why the first conversation is technical. We look at what data exists, where the system would run, who will own it, how outputs will be checked, and what would make the project a bad idea. If no is the right answer, we say it before a build starts.
We are AI-native in our own delivery system: internal agents, evaluation flows, review gates, and documentation patterns we use every day. The speed comes from improving the delivery system, not from lowering the review bar.
Enterprise implementation standard.
A fraction of the cost.
The architects on this team came from inside large management consultancies: 10,000+ FTE firms where senior architecture covered engagements from focused builds to 100+ FTE delivery teams. We keep that architecture discipline and remove the machinery around it: partner leverage, account layers, bench utilisation, and multi-year retainer pressure.
You pay for the work that changes the system: scoping, architecture, engineering, evaluation, review gates, and handoff. Scope is bounded, payments are milestone-gated, and commercial decisions happen at gates, which is why our cost lands at roughly a quarter of comparable enterprise rates.
You get senior AI engineering expertise from day one, without sourcing, interviewing, onboarding, or carrying specialist hires for work that may only need them part-time or for a short phase. Every engagement has access to a senior architecture group that shapes the solution before and during the build.
We pair with your team, not around them. If you do not have an AI or data team, we can build from zero. If you already have data, ML, or platform engineers, we work inside your delivery model and leave the architecture, code, evaluation harnesses, and runbooks in a shape they can operate and extend.
The first step is always
a feasibility call.
2 hours. No cost. No commitment. We'll review your business idea, examine your tech stack, and tell you honestly whether AI makes sense for your case, and what the right next step should be.