AI Discovery Sprint:
Validate Before You Build.
Most AI projects fail not because the technology does not work, but because no one answered the hard technical questions before committing serious budget. The Discovery Sprint answers those questions in two weeks.
Fixed scope.
Fixed price.
Single use case on a provided dataset. The recommended starting point for most projects.
- One use case, one dataset
- Full six-deliverable set
- Two-week fixed timeline
- IP ownership transferred to you
Two use cases, complex data environments, or multiple source systems requiring integration.
- Up to two use cases
- Complex multi-source data handling
- Comparative architecture options
- Full six-deliverable set for each use case
Regulated data environments (HIPAA, GDPR), multi-system integration, or multi-team validation programs.
- HIPAA or GDPR-compliant handling
- BAA executed before data transfer
- Multi-use case validation
- Extended stakeholder presentations
Six deliverables.
Every sprint. No exceptions.
Technical Feasibility Assessment
A written analysis of whether your target outcome is achievable with machine learning, what accuracy level is realistic given your data, and what the primary technical risks are. We tell you when the answer is no.
Data Readiness Audit
An assessment of your existing data covering volume, quality, labeling status, feature relevance, and preprocessing requirements. This is frequently the most valuable output. Data problems found during a sprint save significant budget in mid-project rework.
Working Prototype
A functional model trained on your actual data demonstrating baseline performance with measured accuracy metrics. Not a demo on public data. Not a pre-trained model on generic benchmarks. Your data, your use case, real numbers.
Architecture Recommendation Report
The recommended production technical stack, infrastructure requirements, estimated training timelines, deployment architecture, and integration approach for moving into a full build.
Cost and Timeline Estimate
A scoped estimate for the full Project Build with milestone breakdown, resource requirements, and realistic delivery timeline. You can use this to evaluate our proposal against other vendors on equal terms.
Go or No-Go Recommendation
A clear, documented position on whether to proceed with a full build, and why. If the answer is no-go, we document what would need to change for the project to become viable. This is the most important deliverable.
Two weeks.
Six stages.
Kickoff and Data Ingestion
We receive your data, execute NDAs and any data processing agreements required, and run a technical kickoff call to align on objectives and success criteria. A shared project workspace is established for documentation, code, and async communication.
Data Audit and Preprocessing Review
Your dataset is assessed for volume, quality, class balance, feature relevance, labeling status, and leakage risk. We document findings and flag any issues that could limit model performance. If data quality is a hard blocker, you know by Day 4 before any model work begins.
Prototype Development
A working baseline model is built using your data. We test multiple technical approaches where the evidence warrants it, for example comparing a fine-tuned language model against a classical ML approach for a classification task, and document measured performance metrics for each.
Architecture Design
Based on prototype findings, we design the recommended production architecture: model serving approach, data pipeline design, infrastructure requirements, integration patterns, and estimated ongoing operational cost.
Report Writing and Internal Review
We compile the full deliverable set: the written feasibility assessment, data audit findings, architecture recommendation, cost and timeline estimate for the full build, and the go or no-go recommendation with documented reasoning.
Final Presentation
A 60-minute walkthrough of all deliverables with your technical and business stakeholders. We present the prototype results live, explain all findings, and make the go or no-go recommendation with complete supporting documentation.
The most expensive AI
mistake is building first.
The pattern appears consistently across failed AI projects. A company identifies an opportunity, commits a vendor or internal team, and allocates a six-month build budget. At month three, the data turns out to be insufficient at the volume needed. Or the accuracy ceiling with available data is 71% when the use case requires 90%. Or the integration with existing systems is three times more complex than the initial scope assumed.
The project is either cancelled after spending $60,000 to $200,000, or it is shipped anyway, underperforms, and gets quietly shelved.
A Discovery Sprint surfaces these problems in two weeks for $3,000 to $8,000. The math is straightforward. Every client who has started with a sprint and then proceeded to a full build has told us the sprint paid for itself in avoided rework alone.
Discovery sprints that
became production systems.
Clinical Note Readmission Prediction
A regional hospital network asked whether unstructured clinical discharge notes across three EHR systems could predict 30-day readmission risk. Data was inconsistently formatted and heavily abbreviated.
Sprint outcome: Go. Preprocessing pipeline for EHR normalization scoped at three weeks. Full build recommended. System now in production at 84% accuracy, used in daily discharge planning rounds.
Transaction Fraud Detection Validation
A payments platform suspected their rule-based fraud system was missing 15 to 20 percent of fraudulent transactions while generating false positives that damaged legitimate user experience.
Sprint outcome: Go. Three underused data features identified with high predictive signal. Full ensemble model now in production. Fraud losses down 28% in the first six months.
Demand Forecasting Feasibility
A retail group with four years of transaction history across 14 product categories wanted to know whether ML-based demand forecasting could outperform their existing spreadsheet-based inventory model.
Sprint outcome: Go with caveats. Six categories flagged as data-insufficient. Recommended building on the eight viable categories first. Forecast error reduced from 22% to 9% on priority categories after full build.
When a discovery sprint
makes sense.
Book a Discovery Sprint if you are
- Considering your first machine learning investment and need technical validation before committing a full budget
- Preparing for a fundraising round and need an investor-ready proof of concept
- Evaluating multiple vendors and want comparable technical deliverables from each
- Uncertain whether your existing data is sufficient for model training
- Working with a budget ceiling and need to know what is achievable within it
A sprint may not be the right starting point if you
- Do not have any data yet (a data strategy engagement is a better first step)
- Have already done rigorous internal technical validation with a credible team
- Are ready to build and need a proposal rather than feasibility validation
If you are unsure which path fits your situation, the 30-minute strategy call will tell you. We will give you a direct answer.
Before you reach out.
A standard Nexitelligence Discovery Sprint starts at $3,000 for a single use case on a provided dataset. Extended sprints covering two use cases or more complex data environments range from $5,000 to $8,000. Enterprise engagements involving regulated data such as HIPAA or GDPR-covered datasets, or multi-system integration, are scoped and priced on custom scope after an initial technical review.
A free consultation is a conversation about possibilities. A Discovery Sprint produces a working prototype trained on your actual data, a written data readiness audit, an architecture recommendation report, a scoped cost estimate for a full build, and a documented go or no-go recommendation. It is a billable engagement that produces tangible deliverables you own and can use to compare vendors on equal terms.
For a Standard Sprint we need your relevant dataset, a brief on the business problem you are trying to solve, and access to one or two subject matter experts for roughly four hours total across the two weeks. We assess whether your dataset is sufficient during the first two days and tell you immediately if it is not.
We document it clearly and tell you what data you would need and how to collect it. A data readiness audit that concludes your current data cannot support this use case reliably is a genuinely valuable outcome. It stops you committing a full build budget to a project that would fail at month three. That finding is part of the sprint deliverable set regardless of outcome.
Yes. The architecture recommendation and cost estimate are yours to use however you choose. We would rather help you make an informed decision than win business from a client who is not confident in our approach.
Healthcare (clinical NLP, medical imaging, HIPAA-compliant AI), financial services (fraud detection, credit risk, AML), real estate (automated property valuation, document processing), e-commerce (recommendation engines, demand forecasting), and education technology (dropout prediction, adaptive learning systems). See our case studies for documented results from each vertical.
Ready to get a clear answer?
30 minutes. No pitch. We will review your use case and tell you whether a Discovery Sprint is the right starting point.
Book a Free Strategy Call