Pristine AIFive ways we build.
Named by what you get, not by the tools we use. Most engagements combine two or three.
AI-Native Product Engineering
New products built AI-ready from day one.
We design the data model, APIs, and agent layer together, so AI features ship with the product instead of being added later. It's the same way we built Pristine Data AI.
What's included
- Product architecture
- Typed actions agents can call
- Agent layer with tools, memory, and guardrails
- Evals and tracing
- Web, mobile, and internal tools
Pristine Data AI, and the proactive product we built with an HR technology company serving BFSI enterprises.
Legacy Modernization
On-prem to cloud, rewritten so AI can act on it.
We move on-prem systems to the cloud and rewrite the code underneath, so an AI layer can safely read from and write to it. Lift and shift moves the problem. A rebuild removes it.
What's included
- Code and system discovery
- Control-layer and backend rewrites
- Cloud migration
- An AI layer once the foundation can support it
Hydrolec, where a decade-old equipment monitoring system was rebuilt on AWS with a live AI copilot.
AI Agents and Copilots
Agents that act inside your workflows, not chat widgets.
Agents with typed tools, approval steps, and write-back to your systems of record. A person signs off on anything that changes data. Engine-agnostic: you own the agent, the data, and the IP.
What's included
- Workflow agents
- Operational copilots
- Customer-facing support and sales agents
- Approval steps and audit trails
More than 100 agents in production on Pristine Data AI, plus the Hydrolec copilot.
Cloud and DevOps
Dedicated pods and managed DevOps.
A dedicated pod that ships with your team, or managed DevOps for the platform you already have. Our team includes certified cloud engineers and engineers with prior financial services client work.
What's included
- Infrastructure as code
- CI/CD
- Monitoring and on-call
- Cost reviews
- Security patching
The AWS platform behind Hydrolec, and the infrastructure Pristine Data AI runs on.
Data Engineering
Pipelines and data foundations for AI.
Ingestion, cleaning, modeling, and quality checks, so agents and analytics work from data you can trust. Most AI pilots stall on the data, not the model.
What's included
- Batch and streaming ingestion
- Deduplication and validation
- One shared schema
- Schema, freshness, and lineage checks
A digital experience platform's aging CRM export, validated, corrected, and enriched into 10,853 clean records.
Looking for GTM data enrichment or outbound agents as a product? That's Pristine Data AI. pristinedata.ai
Tell us what you're building.
In one working session, we'll map a concrete first project and what it would take to get it live.