AI Discovery Sprint

Decide what AI is worth building before you fund the build

A focused sprint to turn an AI opportunity into a decision: the user problem, data reality, prototype evidence, delivery risks and a prioritised next step.

Plan an AI discovery sprint See case studies

Use the sprint to make one investment decision well

Good fit

The opportunity is credible but not yet proven

A business problem and potential users are known.
Data, feasibility or delivery risk could still change the investment decision.
A sponsor can act on a recommendation to build, change direction or stop.

Bring to the brief

Evidence, not a perfect specification

The decision you need to make and the cost of getting it wrong.
The available data, systems and subject-matter access.
Known privacy, reliability, procurement or timing constraints.

Receive

A decision package, not a polished demo

A scoped use case and testable success criteria.
Evidence on data readiness, technical approach and delivery risks.
A prioritised recommendation with the next path clearly named.

One

investment decision in focus

1–3 wk

typical focused discovery window

Evidence

data, feasibility and risk

Next step

build, integrate, change or stop

When to start with discovery

Product leaders testing high-impact AI bets

You need practical experiments, not open-ended research. We frame hypotheses, build MVPs, and define investment-grade next steps.

CTOs scaling beyond dashboard-level analytics

Your team needs robust ML architecture, delivery discipline, and production integration into existing systems.

Founders shipping data-driven products

You need outcomes quickly: use proven models where possible, customize where needed, and avoid overengineering.

What you get

A framed user and business problem with a measurable decision criterion
Data and systems assessment, including critical access or quality gaps
A proportionate prototype or experiment where it reduces uncertainty
Technical options with assumptions, cost drivers and delivery risks
A prioritised roadmap: build, integrate, reshape the hypothesis or stop
A brief that can move into AI workflow integration or MVP delivery when justified

What the sprint can unlock

The aim is a useful decision, not a polished demo: continue, change direction, integrate into an existing product, or stop before costs grow.

Frame the decision

Identify the user decision, desired evidence and constraints that should govern the work.

Clarifies what must be true before investment makes sense.

Agreed success and stop criteria
Data, risk and stakeholder map
A bounded experiment plan where needed

Test the riskiest assumption

Use a small prototype, data slice or workflow test only where it changes the decision.

Avoids producing a demo that cannot influence scope or investment.

Explicit assumptions and evidence
Acceptance criteria for a later build
A clear account of limitations

Choose the next path

Turn the evidence into a recommendation and a practical first delivery scope.

Ensures discovery closes with a decision rather than an open-ended research queue.

AI workflow/RAG integration where a working product exists
An MVP scope where a new product is justified
A documented no-build or defer recommendation when evidence is weak

Anonymized case snapshot

Applied AI & ML · Food Retail

Major food retail chain feedback intelligence

We converted large-scale customer feedback into structured, decision-ready intelligence for business teams operating across locations and languages.

Processed over 1 million customer comments across regions
Built multilingual translation, sentiment, emotion, and category pipelines
Combined AI outputs with statistical trend and variance analysis
Delivered reusable dashboards for repeatable decision support

Explore more case studies ->

1M+

Comments analyzed

Multi-lang

Pipeline support

Interactive

Business dashboards

Reusable

Analytics framework

Pragmatic AI methodology

We prioritize business value, reliability, and cost control over hype. Every model choice is explicit and testable.

State-of-the-art when it helps

We use modern models and frameworks when they improve quality, speed, or accuracy for your use case.

Proven methods when they are enough

We avoid unnecessary complexity and choose reliable approaches that reduce cost and operational risk.

No black-box delivery

You get transparent assumptions, measurable acceptance criteria, and clear reasoning behind architecture decisions.

Technology we use in production

ML Libraries

scikit-learn

XGBoost

Hugging Face

PyTorch

AutoML

H2O

Auto-sklearn

Azure AutoML

SageMaker

Pipelines

Python

Airflow

PostgreSQL

Feature stores

Analytics

Metabase

Dashboards

Trend analysis

Variance checks

Infra

AWS

GCP

Azure

Docker / K8s

Governance

GDPR-aware design

Monitoring

Model drift checks

Auditability

What we won't do

We focus on business outcomes. We avoid engagements that create activity but no durable value:

Research-only work with no production or product path
Rebuilding models from scratch when adaptation is clearly faster
ML experiments without agreed success metrics and decision criteria

Continue only with the path the evidence supports

Existing product and knowledge base

Integrate an AI workflow or RAG

When the sprint confirms an integration can improve a working process.

Explore AI workflow integration →

New product opportunity

Launch a focused AI MVP

When the user problem, ownership and first release are defined enough to build.

Explore AI MVP delivery →

Give the next AI investment a decision-ready starting point

Bring the business question, known data and constraints. We will shape a focused AI discovery sprint around the decision you need to make.