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AI MVP

AI MVP Development

FISTA Solutions builds AI MVPs designed to produce an answer rather than a demo: the smallest working system that tests whether AI solves your problem, evaluated against a real baseline, with a written conclusion that includes recommending against continuing when the evidence says so.

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150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does AI MVP development include?

AI MVP work defines the question and success criteria, assembles evaluation data, builds the smallest working system on real data, tests it with real users, and produces evidence plus a recommendation on whether to proceed, pivot, or stop.

  1. 01

    Question and criteria

    What is being tested and what result would justify proceeding, agreed in writing before building.

    Definition
  2. 02

    Evaluation data

    Real cases with expected outcomes assembled first, because they are the reference for everything after.

    Evidence
  3. 03

    Smallest working system

    Real data, real integrations where necessary, real users — not a prototype that simulates the hard parts.

    Build
  4. 04

    Measured results

    Quality against the baseline, cost per unit of work, and usability observations from actual use.

    Measurement
  5. 05

    Written recommendation

    Proceed, pivot, or stop, with the reasoning and the evidence behind it.

    Outcome

Requirements

Which requirements shape AI MVP development?

AI pilots most often fail by proving nothing: no baseline, no evaluation data, and a prototype that avoided the difficult cases. Requirements cover a falsifiable question, real data including hard cases, cost measurement, and a conclusion the organization can act on.

AI MVP: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA builds to it
Falsifiable questionVague pilots always 'show promise'.A specific question with a threshold agreed up front that would mean stopping.
Real data and hard casesCurated demos mislead.Evaluation on real data including the difficult and ambiguous cases the production system will face.
Baseline comparisonAI must beat what exists today.Current process or simple alternative measured on the same cases for comparison.
Cost measurementA working system can still be unaffordable.Cost per unit of work measured during the pilot and extrapolated to production volume.
Actionable conclusionPilots that end ambiguously stall programs.A written recommendation with evidence, including stopping when that is what the data supports.

Where AI fits

How should you sequence AI MVP development?

Run an AI MVP when the answer genuinely matters and is not already known. The sequence that produces a real answer is question, evaluation data, smallest system, measured comparison, then conclusion.

  1. 01

    1. Write the question

    Specific and falsifiable, with the threshold that would mean stopping stated in advance.

  2. 02

    2. Assemble evaluation data

    Real cases including hard ones, before anything is built.

  3. 03

    3. Build the smallest system

    Real data and integrations, avoiding shortcuts that hide the actual difficulty.

  4. 04

    4. Measure against the baseline

    Quality and cost compared with the current process on the same cases.

  5. 05

    5. Conclude honestly

    A written recommendation, including stopping when the evidence points that way.

Cost and timeline

How much does AI MVP development cost, and how long does it take?

Cost is bounded deliberately because the purpose is an answer rather than a product; timeline is typically weeks. FISTA does not quote blind: the scoping call returns the question, success criteria, and a fixed-scope estimate.

The budget is bounded by design. An MVP that grows into a product before the question is answered defeats its purpose and spends money that should have waited for evidence.

A negative result is a good outcome. Learning in weeks that AI does not solve this problem at acceptable cost saves a program, and FISTA reports that plainly rather than finding encouraging framing.

Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.

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Delivery

How does FISTA deliver an AI system?

FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.

  1. 1

    Discover and define

    Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.

    Output

    Specification, golden set, estimate

  2. 2

    Design the system

    Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.

    Output

    Architecture, model decision record

  3. 3

    Build and evaluate

    Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.

    Output

    Eval reports, working system

  4. 4

    Release and monitor

    Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.

    Output

    Production AI system with SLOs

Why FISTA

Why choose FISTA Solutions for AI MVP development?

FISTA builds AI MVPs that answer a falsifiable question with real data, measured cost, and a recommendation that may be to stop. Work is contracted through a US entity with full IP assignment.

AI MVP specifics

  • The question and the stop threshold are agreed in writing before building, so the pilot can genuinely fail.
  • Evaluation uses real data including hard and ambiguous cases rather than a curated demonstration set.
  • Cost per unit of work is measured during the pilot and extrapolated to production volume.
  • The deliverable includes a written recommendation, including recommending against proceeding when evidence supports that.

How FISTA engineers

  • Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
  • AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
  • Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
  • One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.

What you get as a client

  • 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
  • A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
  • US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
  • Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.

Clear answers

What buyers ask before an AI build.

Straightforward guidance for evaluating scope, fit, and the next step.

01How long should an AI MVP take?

Weeks, not quarters. The purpose is to answer a question, and a pilot that stretches into months has usually become a product build without the evidence that should have justified it.

02What do we get at the end?

A working system on real data, the evaluation evidence, the measured cost per unit of work, and a written recommendation to proceed, pivot, or stop.

03What if the answer is that AI does not work here?

That is a valuable result delivered cheaply, and FISTA reports it plainly. Programs are damaged far more by pilots that produce encouraging ambiguity than by clear negative answers.

04Can the MVP become the production system?

Sometimes the foundations carry forward, but an MVP is built to answer a question rather than to scale. FISTA states which parts are reusable in the recommendation.

05Do we need our own data?

Yes, real cases including hard ones. Pilots run on curated or synthetic data produce results that do not survive contact with production.

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Related capabilities

MVP developmentThe same discipline applied to non-AI product bets.AI consulting and strategyDeciding which questions are worth piloting at all.AI product developmentBuilding the product once the bet is proven.

Scoped in writing before you commit

Get a real answer in weeks, not a promising demo.

Bring the question and real data. The scoping call returns success criteria, an evaluation plan, and a fixed-scope estimate.

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