Rapid AI MVP
A 28-day sprint on one narrow use case. We work from your own material and an agreed test of success, and build a working product your team can judge on real cases. You keep the source code.
A narrow proof using real material
Best suited toTeams that can provide representative material and a workflow owner, but need evidence on feasibility, usefulness and risk before a larger commitment.
Not designed forA compressed full product roadmap, a polished demo built only on ideal examples, or a fixed promise made before the source material and edge cases have been inspected.
Prove one workflow before you build more.
Focused intake
We start with the actual material your team works from: drawings, BOQs, tenders, contracts, claims, compliance packs, reports, or exported system data.
The scope is deliberately narrow: one document type, one workflow, one measurable outcome, and enough awkward cases to learn what production will require.
Sprint build
We build the smallest useful workflow: document ingestion, extraction, reasoning, review screens, outputs, and the guardrails needed for a team to trust the result.
For construction, that might be an AutoBOQ-style drawing-to-quantity flow. For other verticals, it may be contract triage, tender comparison, claim evidence extraction, or compliance pack review.
Practical validation
The sprint answers business questions as well as technical ones. Can the workflow produce a result someone can review? Does it save enough time? Would the people doing the work use it?
You leave with a working prototype, source code ownership, lessons from real files, and a clear scale, pivot, or stop recommendation.
Path to deployment
If the sprint works, we turn it into a deployment plan: integrations, permissions, evaluation sets, review roles, ownership, and the support needed after launch.
If it does not work, you still have evidence. You will know which document assumptions, data gaps, or manual judgments block the opportunity before spending months on a full build.
What the engagement includes
A rapid AI MVP exists to support a decision. Over 28 days it takes one recurring workflow, one group of users, representative files and an explicit test of success, and produces enough working software to decide whether the workflow deserves production investment.
Delivery process
- 01
Narrow the decision
We define the user, trigger, input material, reviewable output, baseline, edge cases, and the single decision the prototype must help the team make.
- 02
Build the thinnest complete flow
The prototype includes enough ingestion, model or retrieval logic, interface, and review behaviour to test the workflow end to end, rather than one isolated technical step.
- 03
Test representative cases
Good, difficult, and failed examples are reviewed with the workflow owner. Assumptions and exceptions are documented instead of hidden behind an average score.
- 04
Recommend scale, pivot, or stop
The handover states what worked, what failed, which production controls are missing, and the evidence required before the next investment.
Deliverables
- Written scope with user, workflow, inputs, outputs, exclusions, and success criteria
- Working end-to-end prototype using agreed representative material
- Evaluation notes covering good cases, edge cases, and failures
- Source code and practical handover documentation
- Scale, pivot, or stop recommendation with production requirements
Measurement and engagement shape
The prototype is successful if it resolves the agreed uncertainty. That may be task completion, a review-time comparison, extraction quality on a defined sample, operator acceptance, or proof that an assumption is false.
A sprint proposal is issued only after the workflow and sample material are understood. The scope states what will be working, what remains simulated or manual, and what would still be required for secure production use.
Evidence and preparation
Questions about Rapid AI MVP
What makes a document AI sprint different from a typical MVP?
It starts with your real files and one narrow workflow outcome, and it is built to answer a decision: can AI produce a result your team can review, and does it save enough time to be worth building properly?
How narrow should the first sprint be?
Narrow enough to judge quickly: one document type, one workflow, one user group, clear success metrics, and enough edge cases to expose the production risks.
What do we own after the sprint?
You own the working prototype, the source code, the documentation and the test results from your documents. If the sprint works, we turn it into a deployment plan and agree how the system will be supported.
Go deeper with the free course:
AI for Construction & BOQOther services
- AI Agent Development ServicesAgents that read files, extract facts, draft outputs, work with your systems and pass uncertain cases to a person.
- AI Strategy & RoadmappingFind out where AI is worth using in your work, what is technically realistic, and what to test first.
- RAG Development & AI Data InfrastructurePipelines, retrieval, evaluation and monitoring that keep AI accurate on your documents after launch.
Start with one workflow.
Tell us which piece of work you’d like AI to take on. We’ll reply with what a system could do, what it should leave to a person, and what a first version would take to build.