AI PoC & MVP Delivery

Turn AI ideas into production‑ready PoCs and MVPs that prove value fast

Identify and prioritise AI use cases by impact, feasibility, and risk
Design lean PoCs and MVPs that integrate with your existing stack
Create a clear path from experiment to production and scale

When an AI PoC or MVP is the right next step

Best suited to leaders who want to move beyond slides and lab experiments to real, measurable AI impact.

You need to prove AI business value quickly

You’re under pressure to demonstrate tangible ROI from AI within a quarter or two, not run open‑ended experiments.

You have ideas but lack a delivery blueprint

You see multiple AI opportunities across the business but need help prioritising and shaping them into executable PoCs and MVPs.

You want to de‑risk AI before scaling

You plan to invest significantly in AI and want a low‑risk way to validate assumptions, data readiness, and vendor choices first.

You need to integrate AI into existing products

You own digital products or platforms and want AI features that plug cleanly into current architectures, workflows, and SLAs.

You require strong governance and compliance

You operate in a regulated or risk‑sensitive environment and need AI solutions with clear controls, auditability, and oversight.

You want your teams enabled, not sidelined

You want external expertise that partners with your engineering, data, and product teams so they can own and extend the solution.

You’re evaluating AI vendors and platforms

You need an independent perspective to compare LLMs, tooling, and cloud services through the lens of a real PoC or MVP.

You must manage cost and complexity

You want lean, right‑sized PoCs and MVPs that avoid over‑engineering while keeping a clear path to scale.

You value disciplined, product‑led delivery

You expect clear milestones, governance, and measurable outcomes—not a black‑box AI experiment.

Example use cases

Where an AI PoC or MVP delivers fast, defensible value

SaaS / B2B

Intelligent lead scoring for B2B sales

Design and deliver a PoC that ingests CRM and product usage data to score and prioritise leads, then ship an MVP that plugs into existing sales workflows and reporting to prove uplift in conversion and pipeline quality.

E‑commerce / Consumer

AI‑assisted customer support triage

Prototype an LLM‑powered assistant that classifies and routes tickets, suggests responses, and flags risk. Move to MVP with guardrails, integration into your helpdesk platform, and measurable reductions in handle time and backlog.

Retail / Manufacturing

Demand forecasting for operations

Run a PoC using historical sales, seasonality, and external signals to predict demand at SKU or location level. Progress to MVP with automated data pipelines, exception reporting, and dashboards for planners and finance.

Financial Services / Insurance

Document intelligence for compliance teams

Build a PoC that uses NLP and LLMs to extract key terms, obligations, and risks from contracts and policies. Evolve into an MVP with review workflows, human‑in‑the‑loop validation, and audit trails aligned to regulatory standards.

Digital Products / Media

Personalised in‑app recommendations

Test recommendation models on a subset of users to improve engagement and retention. Move to MVP with real‑time inference, A/B testing, and guardrails to protect brand, fairness, and performance SLAs.

Our approach

From idea to AI PoC and MVP with a clear path to production

We combine product thinking, data and ML engineering, and change management to ensure your AI PoC or MVP is technically sound, commercially relevant, and ready to scale.

Align on business outcomes and use‑case priority

Work with your leadership and domain experts to clarify objectives, quantify value, and select 1–3 high‑leverage AI use cases. Define success metrics, constraints, and a realistic scope for PoC vs. MVP.

Solution design, data readiness, and architecture

Assess data availability and quality, choose appropriate models and vendors (build vs. buy), and design a target architecture that fits your stack, security posture, and governance requirements.

Rapid PoC build and validation

Implement a lean PoC that proves the core AI capability using production‑like data. Run structured evaluation against baseline metrics, capture user feedback, and refine the approach before committing to an MVP.

MVP delivery with integration and guardrails

Extend the PoC into an MVP with robust data pipelines, APIs, and UI integrations. Implement monitoring, observability, security, and human‑in‑the‑loop controls so the solution is safe, auditable, and supportable.

Rollout, adoption, and roadmap to scale

Plan a controlled rollout, enable your teams, and embed the AI solution into day‑to‑day workflows. Document operating models and a roadmap to full production, including ownership, SLAs, and future enhancements.

Business Outcomes

A validated AI use case with clear, quantified business impact
A production‑ready PoC or MVP integrated into your existing ecosystem
Reduced technical, operational, and compliance risk for AI initiatives
A pragmatic roadmap from experiment to scaled AI capabilities

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