Selected work
AI inside products that already have customers
Every engagement here started with software that was already live: existing workflows, existing integrations, existing constraints. That is the interesting part.
- 8
- AI builds inside products that were already live
- 5
- of them vertical B2B SaaS companies
- 2018
- the year Datics started in data science and AI
Vertical SaaS, dental and veterinary practice communications
AI communication intelligence inside a live practice platform
Established communication intelligence software serving dental and veterinary practices. AI needed to become part of existing practice workflows rather than a separate application.
Vertical SaaS, dealership operations
Modernizing an established dealership SaaS platform without rebuilding the core product
A multi-year product engineering partnership inside a live dealership platform: existing customer workflows, dealership operations, integrations, appointments, reporting and customer intelligence.
Vertical SaaS, dealership intelligence
Competitive inventory intelligence for dealership sales teams
A web portal giving dealers detailed visibility into competitor inventory across automobiles, RVs, powersports and marine.
Vertical SaaS, advertising technology
One place to run advertising across fragmented OTT platforms
An over-the-top advertising product that consolidates campaign data so advertisers can run across many platforms at once.
Vertical SaaS, financial market intelligence
Data-driven decision support for investors and SMEs
A SaaS platform built to help investors navigate complex financial markets, where the value is in turning dense market data into decisions people can act on.
Cloud management software
Quality and reliability work inside a live cloud management product
A software quality and testing strategy for an existing web application portal, the kind of groundwork that makes later AI work safe.
How we describe work
Client type, problem, capability, cloud, outcome
We avoid vanity metrics. Every case study states the client type, industry, existing product, problem, AI capability added, constraints, cloud and deployment environment, the Datics role and the outcome.
- Peerlogic · Vertical SaaS, dental and veterinary practice communications
- Problem. Calls were being missed and bookings lost, and any AI answer had to arrive inside the software practices already run rather than as another tool staff would have to learn. Cloud. Google Cloud, deployed inside the customer's own environment, HIPAA-aligned handling of call and patient-adjacent data. Outcome. Calls that previously went unanswered turn into booked appointments, handled by the product the practice already uses every day, with no new application for staff to adopt.
- Dealership Toolkit Portal · Vertical SaaS, dealership operations
- Problem. The platform had to keep modernizing for existing customers while dealers ran their day on it, with performance data spread across disconnected dashboards. Cloud. Multi-tenant cloud deployment (React, Node.js, PostgreSQL) serving dealer networks across four vehicle categories. Outcome. Dealers see where activity converts and act on it inside one product instead of several disconnected dashboards, on a platform that kept modernizing without a rebuild.
- MyLotSpy · Vertical SaaS, dealership intelligence
- Problem. Competitor inventory data sits across many dealer websites in different shapes and goes stale within hours, so sales teams priced against guesswork. Cloud. DigitalOcean-hosted multi-tenant portal (React, Node.js, PostgreSQL) with scheduled crawling and ingestion pipelines. Outcome. Sales teams price and position stock against what is actually on competing lots.
- Drive OTT, built with Coast Technologies · Vertical SaaS, advertising technology
- Problem. Every downstream OTT platform reported differently, so advertisers stitched exports together by hand to answer basic questions about campaign performance. Cloud. AWS-hosted multi-tenant platform (React, Node.js) with per-integration data pipelines. Outcome. Advertisers run and read campaigns across platforms without stitching exports together by hand.
- COSMIC MarketVerse AI · Vertical SaaS, financial market intelligence
- Problem. High-volume, fast-moving market data was accurate but unreadable for users who are not analysts, so the analysis was not reaching a decision. Cloud. Microsoft Azure, with Azure AI Search for retrieval, a Neo4j knowledge graph and FastAPI services behind the market-data pipelines. Outcome. Users get to a defensible decision faster, without leaving the platform to assemble their own analysis.
- AMove · Cloud management software
- Problem. Critical paths built up over years had uneven coverage, so every change carried risk in a product customers depended on. Cloud. Customer's production cloud environment, multi-cloud storage and transfer workloads, tested with Selenium, Cypress, JMeter and OWASP ZAP. Outcome. A more predictable product and a codebase that can absorb change with less risk.
Your customers already expect AI from your product.
Let us make it real without rebuilding what already works.