We design and build time-boxed AI agent Proofs of Concept and production-ready MVPs that answer your highest-risk business questions before you commit full engineering budget to a build.
A PoC is a time-boxed experiment — not a mini-product. Its only job is to answer: can this AI agent do the specific thing we need it to do, on our data, within our constraints? Everything else is noise until that's answered.
Building a full product without validation costs an average of $800K. In 72% of cases, it fails. A PoC costs $10–20K and a focused MVP $30–150K. The spend delta is the cost of certainty — before you commit the full resource.
Deloitte 2024: 60% of PoC success is linked to upfront data readiness. We audit your data availability, quality, and structure before scoping any build — eliminating the leading cause of AI PoC failure before it can happen.
AI-assisted PoC development compresses design-to-demo cycles by up to 50%. Reaching a go/no-go decision in 4–6 weeks instead of 3 months means faster pivots, earlier investor traction, and compounding competitive advantage.
65–75% of MVPs failed to progress in 2025 — not because they were slow, but because AI was bolted on rather than engineered in. We build MVPs where AI is an internal system component, not a feature layered over fragile architecture.
Every PoC and MVP we build is scoped for forward compatibility — the architecture, data pipelines, and agent logic are designed to plug directly into full autonomous deployment without a rebuild when you're ready to scale.
The cause is rarely technical. Scope creep, missing success criteria, data that isn't ready, stakeholders misaligned on what a PoC is supposed to prove, and PoCs that are treated like pre-products — these are the failure modes. We've seen them all. Our process is designed specifically to eliminate them: define the question, scope the data, set the acceptance threshold, run the experiment, and deliver a crisp go/no-go decision.
The average organisation scraps 46% of its AI proof-of-concepts before production — and only 26% of organisations have the internal capability to move beyond PoC to a production-grade deployment at all.
Up from 17% in 2024. The primary cause: no measurable acceptance threshold defined before build started. If you can't define what "success" looks like before you begin, you can't make a confident go/no-go decision at the end.
Gartner cites escalating costs and unclear value as the primary causes. Overloading a PoC with features makes it impossible to test anything clearly. A PoC has one job: answer one question. Every feature beyond that delays your decision.
McKinsey 2024: PoCs built without a forward-compatible architecture create a second full build when it's time to scale. We design every PoC as a slice of the production system — not a throwaway demo.
Most organisations blur these stages and end up with a PoC that behaves like a pre-product — overloaded, slow, and impossible to evaluate. We keep each stage tight, purposeful, and directly connected to the next.
Time-boxed, low-risk experiment. Validates one technical hypothesis against your actual data. Defines acceptance criteria and guardrails before any build starts. Delivers a binary go/no-go decision.
Deployed in a real environment with real users and live data — but scoped to a limited workflow. Tests the business hypothesis and ROI case before full commitment. Includes structured feedback loops and KPI tracking.
A production-aware system built on one core agent workflow, deployed to real users. Validates market demand and collects structured feedback. Architected for direct expansion into full deployment — no rebuild required.
Every engagement starts with acceptance criteria, not architecture. We define the exact KPI your PoC must hit, the guardrails it must stay within, and the go/no-go threshold — before a single line of code is written.
AI-assisted development with senior engineering oversight compresses PoC delivery to 4–6 weeks versus the 10–12 week industry standard. You get a decision in weeks, not quarters.
We audit your data availability, quality, and structure before scoping any build. Deloitte links 60% of PoC success to upfront data readiness — we treat it as a prerequisite, not an afterthought.
Every PoC and MVP is built as a slice of the production system — not a throwaway demo. When you're ready to scale, the architecture, pipelines, and agent logic plug directly into full deployment.
A structured PoC at $10–20K answers the question that would otherwise cost $800K to answer incorrectly. We design engagements to deliver the most critical evidence at the lowest possible spend.
Every engagement ends with a clear decision document — not a demo. Technical findings, business case assessment, scale economics, and a recommended next step backed by evidence from the experiment itself.
Adapted from validated industry methodology. Every sprint ends with a defined deliverable and an evidence-based decision point. No week is a holding pattern.
| Dimension | PoC | Pilot | MVP |
|---|---|---|---|
| Primary Question | Can the tech work? | Does it create business value? | Can we ship this to users? |
| Timeline | 3–6 weeks | 4–8 weeks | 6–12 weeks |
| Typical Cost | $10K – $20K | $25K – $60K | $30K – $150K |
| Environment | Test data, controlled | Real users, limited scope | Production — live users |
| Output | Go / No-Go decision | ROI evidence + scale plan | Live product + user feedback |
| Risk Level | Lowest — narrow scope | Moderate — real stakes | Higher — full product bets |
| When to Use | Feasibility unknown | Feasibility proven, value unproven | Both proven, need users |
| Sector | PoC / MVP Agent Type | Hypothesis Validated | Stage | Outcome |
|---|---|---|---|---|
| Financial Services | Fraud detection agent | Can agent match analyst accuracy at 10x volume? | PoC → MVP | +20% detection |
| Healthcare | Clinical workflow automation | Can agent reduce admin time by 30%+ per clinician? | Pilot → MVP | 49% time saved |
| Legal | Contract analysis agent | Can agent extract key clauses at 95%+ accuracy? | PoC | 60% time saved |
| SaaS Products | LLM feature agent | Does AI feature lift retention measurably in 6 weeks? | MVP | +18% retention |
| E-Commerce | Personalisation agent | Does agent-driven ranking outperform rule engine? | Pilot → MVP | +18% conversion |
| Manufacturing | Predictive maintenance agent | Can agent predict failure 48h+ ahead on live sensor data? | PoC → Pilot | 22% less downtime |
We work with your team to define the exact question the PoC must answer, the KPI threshold that constitutes success, and the data scope required. Every engagement begins with a written scope document — not a kickoff call.
We audit data availability, quality, and structure before any build starts. Forward-compatible agent architecture is designed at this stage — ensuring the PoC is a slice of the production system, not a throwaway experiment.
Agent is built, integrated with your data sources, and run against the acceptance criteria defined in Phase 1. Accuracy, latency, edge cases, and cost per inference are all measured and documented with senior engineering sign-off at each stage.
Every engagement ends with a structured decision document: technical findings, business case model, scale economics, and a clear recommended next step. You leave with evidence, not a demo — and a direct path to production if the PoC passes.
