Who this is for

The sprint exists for a specific buyer: someone who is interested but not yet convinced, and who wants evidence before signing off a production budget.

The business

An SME with real operational data already in a system — an ERP, a document archive, a database, or a mix
A specific workflow in mind that is currently slow, manual, or dependent on one person
Someone internally who can grant access to a data source and answer questions about how the business actually works
A budget decision that needs justifying to a partner, a board, or yourself

The situation

You have seen AI demos that looked impressive and learned nothing from them
You are not willing to commit a five-figure production budget on a slide deck
You want to know how the system behaves on your messy records, not on curated samples
You want a defined end date, not a project that quietly expands

Where we are a poor fit

If you cannot get us access to a real data source within the first week, the sprint has nothing to test and we would be selling you a demo. If nobody internally can say what a correct answer looks like, accuracy cannot be measured and the result will be an opinion rather than a number. If you want ten workflows explored rather than one proven, this is the wrong engagement — the discipline of the sprint is that it does one thing properly. And if you have already decided to proceed to production, skip the sprint entirely and put the money into the build. We will say all of this on the call rather than take the work.

Why most AI projects stall

Failures are rarely about the model. They are about what was skipped at the start. The sprint is designed around these six failure modes, in order to trigger them early and cheaply.

The demo that worked on clean data

It was built on a tidy sample export. Real records have duplicate customers, abandoned product codes, half-finished orders and fifteen years of exceptions. Behaviour on the sample predicts nothing.

Accuracy nobody defined first

"Is it accurate enough?" gets asked after the build, when the answer is expensive. Without a definition of a correct answer agreed up front, every review becomes a debate about impressions.

A pilot with no success criteria

Pilots that cannot fail cannot succeed either. With no threshold set before the work starts, the pilot ends in an inconclusive meeting and the project loses its sponsor.

Data access secured too late

Credentials, network rules and IT sign-off get treated as paperwork for later. They are frequently the longest item on the critical path, and they surface in week six instead of week one.

A prototype that cannot be deployed

It works on a laptop against an extract. The data actually lives on a server that cannot reach the public internet, or under a policy that forbids sending it out. The prototype has nowhere to go.

Nobody trained to operate it

The system ships, the people who would use it were never involved, and no one internally can restart it, adjust it, or explain it. Adoption stops within a month of handover.

What happens, week by week

The sequence matters more than the technology. Access first, build second, evidence third, plan last.

Week 1

Access and scoping

We get read-only access to one real data source and confirm it works — that is the first milestone, not a formality. In parallel we lock the scope: one workflow, chosen because it is the fastest to verify and the easiest for someone to act on.

Read-only credentials issued and connectivity confirmed
The single workflow named and written down, with what it is not agreed too
A definition of a correct answer, agreed with the person who would use it
The accuracy threshold that counts as success, set before any building

Week 2

Build against the real thing

The workflow gets built and pointed at your live records from the first day of implementation. No sample data, no curated extract. Where the data is inconsistent, that inconsistency is part of the finding, not something we quietly work around.

The workflow implemented end to end and running on real records
Data quality problems documented as they appear, rather than patched over
Refusal behaviour built in — the system says it does not know rather than guessing
A short mid-sprint review so you see it working before the sprint ends

Week 3

Evaluation against real data

This is the week that separates a sprint from a demo. We build a test set of real cases from your data with known correct answers, run the workflow across it, and report the score against the threshold agreed in week one. Our ERP assistant is measured the same way — against a 480-case internal test suite run on a real database — and we apply that method to your workflow.

A case set drawn from your records, with expected answers confirmed by your team
Measured results: what it got right, what it got wrong, and where it correctly refused
The failure cases examined individually, since those tell you more than the score does
A plain statement of whether the threshold was met — including when it was not

Week 4

Production plan and handover

We write down what production would actually take: where it runs, what it costs, who operates it, what has to change on your side, and what remains unproven. If the evidence says do not proceed, the plan says that instead — and it is more valuable when it does.

A costed production plan with a timeline and a fixed-price proposal
Deployment options set out — cloud, or fully on-premise, with the trade-offs stated
A security assessment covering access, logging and where your data would sit
Handover of the working sprint build, the evaluation results and the documentation

A two-week variant runs for narrow scopes — a single data source that is already accessible and a workflow with a tight, unambiguous definition. It compresses weeks 1 and 2 and keeps the evaluation and production plan intact. We tell you which variant applies before you commit, not after.

How long it takes

The sprint is the second rung of a ladder. Every stage has a defined end, and each one is a separate decision.

Stage	Duration	What ends it
Assessment	1 week	A written roadmap and a fixed-price proposal. Optional — start here if you are not yet sure which workflow to prove.
Proof-of-Value Sprint	2–4 weeks	One workflow working against your real data, accuracy measured against an agreed threshold, and a costed production plan written.
Production deployment	6–12 weeks	Live system, role-based access, logging, training and handover complete.
AI Operations retainer	Ongoing, monthly	Optional. Monthly review, new reports, maintenance. Cancellable.

You can enter at the sprint without doing the assessment first, provided you already know which workflow you want proven. Nothing at any stage commits you to the next one.

The security model

A sprint means giving an outsider access to real business data. That deserves scrutiny before you agree to it, so here is exactly how the system touches your records.

Read-only by default

Our ERP assistants connect with read-only credentials. They can query and report on your data, but cannot modify records in your ERP. Nothing built during a sprint writes back to your systems.

Least privilege

Access is scoped to the data a feature needs. Queries can be inspected, and data can be allow-listed. A sprint touches one data source, not everything you have.

Cloud or fully on-premise

You choose where the system runs. On-premise deployments can use locally hosted open models so that your data never leaves your environment — decided in week one, not discovered at the end.

Your data is not training data

Where a product uses a hosted model, we use established providers via their APIs — or a local model when you prefer. We do not use your business data to train AI models.

Refusal over guessing

When there isn't enough evidence to answer safely, the system is designed to say so rather than guess. Our ERP assistant's accuracy is measured against a 480-case internal test suite run on a real database, and correct refusals are scored as correct.

PII controls and audit logging

Personal data can be masked and result sets limited. Systems can be configured with audit logging of queries and actions, so you always know what was asked and what it saw.

An honest note on certifications

SiliconBlaze does not currently hold formal certifications such as SOC 2. We state that plainly rather than implying otherwise. What we offer instead is transparent, technically specific documentation of how our systems handle your data — and the option to run everything inside your own environment. Full detail is on our security page.

What you walk away with

Four concrete deliverables, handed over at the end of the sprint. You keep all of them whether you continue to production or stop there. That is the difference between this and a free pilot: a free pilot leaves you with an impression, and the vendor keeps the work.

The working workflow

The system itself, built and running against your real data, with the code and configuration handed to you. Not a video, not a sandbox that expires — the thing that was evaluated.

The measured accuracy results

The test cases drawn from your records, the expected answers, what the system produced, and the score against the threshold you set in week one. Including the failures. You can hand this to anyone who needs convincing, or use it to evaluate a different vendor.

The security assessment

A written account of what access was needed, where data would sit in production, what could run on-premise, and which of your policies the deployment would touch. Useful to your IT function regardless of what you build next.

The costed production plan

Scope, timeline, fixed price, what has to change on your side, and an explicit list of what the sprint did not prove. If the honest recommendation is not to proceed, that is what the document says.

A sprint that ends in a clear "this does not work on your data" has done its job. It cost a low four-figure sum and three weeks instead of a five-figure budget and six months.

Illustrative scenario

Three weeks on one workflow

Illustrative scenario. This example is based on common patterns we see in SME data. It is not a specific, named client, and the figures are illustrative rather than from a single real engagement.

Consider an SME that wants its sales team to stop losing accounts quietly. The workflow proposed is simple to state: produce a weekly list of customers who have passed their usual reorder cycle and are worth a call. Management is interested but has been burned by a previous AI project that demoed well and never shipped.

Week one. Read-only credentials to the ERP database, confirmed working on day three — which is fast, and often the item that slips. Scope is written down to one page: this list, for these account types, on this cadence. Then the part most projects skip. A sales manager is asked what makes an account genuinely worth calling, and the answer turns out to be more specific than "hasn't ordered lately". The threshold is set: of the accounts flagged, a defined majority must be ones the manager agrees are worth a call.

Week two. The workflow is built against live records from day one. The data misbehaves in the way real data does — the same customer exists three times under slightly different names, and seasonal accounts look lapsed every year without being lapsed. Both are documented as findings rather than smoothed over, because both would have derailed a production rollout later.

Week three. A case set is drawn from real accounts, and the manager marks each one as worth calling or not, without seeing what the system decided. The workflow runs across the set and the result is compared to the threshold. Some flagged accounts are wrong, and the reasons are examined individually — that examination is usually more useful than the headline number, because it tells you whether the errors are fixable or fundamental.

The decision. The evidence supports one of three answers: proceed to production, proceed after fixing a specific named problem, or stop. All three are acceptable outcomes of a sprint. Only the last one is expensive to discover any other way.

The point of this scenario is the sequence, not the numbers: get access first, define correct before building, then let real data decide. It is deliberately the opposite of building for six months and evaluating at the end.

What it costs

Published, so you can decide whether to have the conversation. Each stage is a separate decision — nothing commits you to the next.

This page

Proof-of-Value Sprint

from $2,400

2–4 weeks. One data source connected, one workflow built and running against your real data, accuracy and security evaluated, production plan written. Price fixed before we start.

Cheaper first step

Assessment

$390

Fixed price, one week. Data and process review, three prioritised use cases, feasibility and security notes, a 30/60/90-day roadmap, and a fixed-price proposal. Optional if you already know which workflow to prove.

Production Deployment

from $9,000

6–12 weeks. Data integration, role-based access, the assistant and intelligence modules, logging and evaluation, training, documentation and handover.

AI Operations Retainer

from $600/mo

Optional and cancellable. Monitoring, retrieval and prompt evaluation, pipeline maintenance, new reports and workflows, monthly business review.

Prices in USD. If you are billing in EUR, tell us on the call and we will quote in EUR at the equivalent rate.

Prove one workflow first

Two to four weeks, a fixed price from $2,400, and a working system measured against your own records. You keep the evaluation results and the production plan whether or not you continue.

Scope Your Proof-of-Value Sprint

Not sure which workflow to prove? See what the assessment covers first.

Find out if it worksbefore you fund it.