You have an idea for applying AI in a product or process, but it's unclear whether it's technically feasible, how much it will cost, or whether your existing data allows it.
Before investing in development, you want to be sure the AI solution will solve the problem and work exactly the way you need it to.
You need to justify an AI investment to stakeholders, and for that you need proof the solution actually works.
A consultant or vendor claims AI can automate most of the work, and you want to test that on your actual data, not just in theory.
You want to implement AI in your processes and need to decide on an approach and technology: LLM, RAG, ML, or something else.
A competitor has already automated a similar task with AI, and you want to quickly find out whether that's realistic for your case too.
The outcome of an AI project depends on the quality and volume of data, the accuracy of the hypothesis, and the right technical approach. Without validating these factors upfront, there's a real risk of spending time and budget without getting the expected result.
The Sprint covers formulating the hypothesis, assessing the data, building a prototype, measuring results on real cases, and reaching a final decision. You'll receive a full technical report: a roadmap for further development, or a list of what needs to be fixed in the data or infrastructure. If you continue working with FreshTech, the Feasibility Sprint's cost is credited toward the project.
We frame the AI hypothesis in a measurable format: "AI can [perform task X] with [Y%] accuracy in [Z seconds] based on [data W]." This sets clear criteria for evaluating the results.
Then we determine the accuracy level acceptable for your business and check whether a ready-made tool for a similar task already exists and could be cheaper than a custom solution.
We assess the data: volume, quality, structure, and relevance. We check whether there's enough data for training or testing, whether it contains the information you need, and what preparation it requires.
If the data isn't sufficient, we determine whether it's realistically possible to collect it, how long that would take, and what's needed for it. We choose the technical approach: LLM prompting, RAG, fine-tuning, classic ML, or a hybrid, depending on the task.
We choose the optimal and fastest technical approach for testing:
LLM Sprint: configured system prompts plus testing on real cases. Suits tasks that can be solved through a well-formulated prompt to the model.
RAG Sprint: a minimal document indexing pipeline plus testing the quality of answers based on your knowledge base. Suits document search, Q&A, and internal assistants.
ML Sprint: a basic model trained on existing data, with metrics evaluation. Suits tasks that require a custom model or fine-tuning.
We track results on real cases: accuracy, recall, latency, cost per request. Comparison with the baseline: how the task is handled now without AI and what specifically improved.
Edge cases: where AI makes mistakes, how critical that is, and what to do about it. Cost of error: if AI is wrong in X% of cases, what that means for your business.
Go: a clear roadmap for the production solution, with goals, timelines, technical approach, and a cost estimate for the full project.
No-Go: Data Readiness Gap Analysis that lists what's missing in your infrastructure or data to become AI-ready. For example, updating the logging system, collecting specific parameters, or structuring your knowledge base. This saves your team months of work and gives you a clear plan for next steps.
Regardless of the outcome, you get a full technical report.
Who it's for: Fintech, distributors, legal, insurance, and other companies with a steady flow of paper or PDF documents.
Hypothesis: AI can automatically extract details from invoices and contracts with accuracy matching manual processing.
What we test: an LLM or ML approach on your document archive. We measure the accuracy of extracting the required data (amount, date, details, etc.), the error rate, and the processing time per document.
Who it's for: SaaS products with active customer support, companies with a large internal knowledge base.
Hypothesis: an AI assistant answers standard questions from the knowledge base faster than manual search, giving customers an answer in seconds.
What we test: a RAG pipeline built on your documentation, FAQs, and internal knowledge base. We measure answer quality, the share of questions the assistant can answer, and the rate of unanswered queries.
Who it's for: Fintech (credit scoring, fraud detection), HR (candidate scoring), B2B (lead scoring).
Hypothesis: AI scoring is more accurate than manual scoring, and decisions are made in seconds instead of hours.
What we test: an ML model on historical decision data. We measure accuracy and recall by category, comparing against the current baseline.
Who it's for: B2B sales teams with an outbound channel, marketing teams personalizing customer communication.
Hypothesis: AI generates personalized follow-up emails, summarizes calls, or identifies customer intent better than a template-based approach.
What we test: an LLM approach on samples of real communications. We measure generation quality, tone accuracy, and time spent on preparing materials.
Who it's for: support teams, dispatch teams, service companies with a flow of varied requests.
Hypothesis: AI classifies incoming tickets and assigns categories without an operator, with accuracy sufficient for automatic routing.
What we test: an LLM or classic ML approach on your ticket archive. We measure classification accuracy and the number of cases the system can't confidently determine.
A clear plan for the AI project: technical approach, architecture, timeline, cost estimate, and rationale for the technology choices. Ready to support a budget decision or an investor presentation.
A report with test results: accuracy, recall, latency, cost per request. Comparison with the current baseline. Edge cases: where AI makes mistakes and how critical that is.
If AI can't solve your task on your current data, you get a detailed analysis of why, along with an action plan: what to change, which parameters to start collecting, how to structure your existing data to become AI-ready.
Regardless of the outcome, you get a full technical report covering the hypothesis, data assessment, technical approach, prototyping process, metrics, findings, and recommendations.
Hypothesis: AI can automatically classify incoming credit applications into 7 risk categories without an underwriter, with over 85% accuracy.
Data: an archive of 4,200 processed applications with underwriter decisions over 18 months.
What we did: an ML Sprint with a fine-tuned classifier on historical data. On the test set: 89% accuracy, 91% recall for critical categories. Classification time: 1.2 seconds versus 40 minutes of manual review.
Result: Go. Production solution roadmap: 8 weeks, integration with CRM and compliance logging.
Hypothesis: a RAG assistant answers over 70% of support queries based on documentation, without a support agent.
Data: 890 pages of documentation and 3,400 historical support tickets.
What we did: a RAG pipeline built on the documentation, tested on 200 real tickets. Results: 73% accurate answers, 12% escalated to a human due to low confidence, 15% outside the scope of the documentation.
Result: Go, with one caveat. The index needs regular updates whenever new product versions ship, which is built into the roadmap.
Hypothesis: AI identifies key contract details (parties, amounts, dates, terms) with over 90% accuracy and cuts manual processing time from 2 hours to 10 minutes.
Data: 650 contracts of various types in PDF and Word.
What we did: 30% of the documents were low-quality scanned PDFs, which required OCR preprocessing. LLM prototype results: 94% accuracy on structured documents, 81% on scanned ones.
Result: Go, with a condition. A roadmap with two separate flows (for structured and scanned documents), plus a recommendation to standardize scanning.
A No-Go still gives you a concrete, practical outcome you can act on. You get a Data Readiness Gap Analysis: a clear list of what's missing in your infrastructure or data to become AI-ready. What to change in the system, which parameters to start collecting, how much time preparation will take. This saves your internal team months of work and protects you from unnecessary spending.
