Every project starts with an AI Feasibility Sprint to verify if your existing data can deliver the desired results. This step allows us to evaluate hypothesis viability while defining accurate pricing and development timelines.
We focus on two areas: adding AI capabilities to digital products and integrating AI into internal business processes. The first includes smart search, personalized recommendations, and content generation. The second covers document processing, knowledge base–powered responses, and predictive analytics.
Every project starts with an AI Feasibility Sprint to test the hypothesis on real data. The outcome is either a Go with a roadmap, or a No-Go with a Data Readiness Gap Analysis.
RAG architecture generates answers based solely on your company's documents. Human-in-the-Loop for AI Document Processing routes complex cases to a human.
ML models and RAG indexes lose accuracy over time as data changes and prompts become outdated. That's why we deliver AI products on a Setup + Ongoing Support model.
We've delivered projects across Fintech (scoring, KYC), HVAC (maintenance and service automation), SaaS, E-commerce, B2B, manufacturing, logistics, and other industries.
Success metrics for an AI feature (scoring accuracy, search conversion, or something else, depending on the use case) are defined before development begins.
Spiro AI Layer extends processes built on Quick Process Automation or Operations Hub. AI in Product pairs with MVP Starter or Agent-First MVP.
Hypothesis: AI can automatically classify incoming credit applications into 7 risk categories without underwriter involvement, with over 85% accuracy. Data used for validation: 4,200 applications over 18 months.
Solution: an ML Sprint with a fine-tuned classifier trained on historical data. Test results: 89% precision, 91% recall. Classification time: 1.2 seconds vs. 40 minutes of manual review.
Result: Go. Production roadmap is 8 weeks, including CRM integration and compliance logging.
Situation: an accountant spends 2.5 hours a day manually entering data from 80–120 incoming invoices in various formats. Errors are only caught at the payment stage.
Solution: PDFs and scanned invoices → OCR → LLM-based extraction of structured fields → verification → transfer to the accounting system. Invoices with uncertain fields are routed to a human.
Result: exception review time is 20 minutes; accuracy exceeds 95% for standard formats and 88% for non-standard ones. Error rate dropped by 94%.
Situation: most tickets handled by the support team involve routine questions about pricing, limits, and procedures. Response times could stretch to several hours.
Solution: a RAG assistant connected to the product documentation and FAQ. It answers within seconds and escalates low-confidence queries to a specialist with full conversation context.
Result: 70% of questions are resolved by the assistant. Response time dropped to mere seconds.
Situation: only 23% of active users engaged with new product features. 18% of users churned within 2–3 months of signing up.
Solution: an AI Feasibility Sprint confirmed sufficient data volume (12 months, 3,200+ users) to support collaborative filtering. When user behavior signals an unmet need, the system recommends the relevant feature.
Result: feature adoption grew from 23% to 61%. User churn dropped from 18% to 11%.
Situation: 1,200+ sites, 4 years of maintenance logs. Emergency callouts made up 23% of service volume, costing three times more than scheduled repairs.
Solution: a model analyzes maintenance logs, operating conditions, and failure history to predict the probability of equipment failure within the next 30 days. Integrated with the Field Service App.
Result: emergency repairs dropped from 23% to 11% within 4 months.
70%
of companies worldwide
already use generative AI
in their work
Source: Stanford HAI, AI Index Report 2026
We get into the details of your task: what problem needs solving and what data you have. We identify the right product type and recommend where to start, and provide an estimated cost and timeline.
We formulate a hypothesis, assess your data, build a minimal prototype, and measure the results, ending in a Go or No-Go verdict. Its cost is credited toward the project if you move forward with FreshTech.
Once we get a Go, we finalize technical requirements and scope, then set the price and timeline based on your specific task and confirmed data.
We build the AI solution based on the approach validated by the AI Feasibility Sprint. Development is iterative, with regular demos and metric tracking throughout the project.
Launch to production. The metric defined during task scoping is used to measure the impact of the deployed solution. A/B testing is available if needed.
AI solutions require continuous support: knowledge base reindexing, model retraining as performance degrades, and prompt updates.
It depends on the task. AI Document Processing and Context-Aware AI Assistant work well even for small teams: a 20-person company can see fast results if, for example, an accountant is currently spending most of their workday processing invoices. AI Analytics & Prediction requires historical data, so company size affects whether you have a sufficient dataset. The AI Feasibility Sprint will show whether a solution is realistic for your specific case.
