Practice — Data, Analytics & AI
Make decisions on data your team actually trusts.
Summary
Most companies have data. Very few have a decision layer they can trust. The gap is engineering — clean pipelines, defensible metrics, models tied to real business KPIs, and dashboards that leadership actually opens. We build that layer, and we build the AI features that sit on top of it: retrieval assistants, extraction pipelines, forecasting, and agentic workflows grounded in your data.
Polyric ships data and AI as production systems, not experiments. Every pipeline has SLAs, every model has evals, every AI feature has guardrails, cost budgets, and a clear human-in-the-loop fallback for when the answer isn't good enough.
Who it's for —Operations, revenue, and product leaders who want a single trusted view of the business — and AI features grounded in that data, not in demos.
Outcomes
- ▸One trusted metrics layer
- ▸AI features with measurable business KPIs
- ▸Documented eval suite for every model
What we deliver
Business capabilities, not line items.
Each deliverable is a business outcome you can name — and measure — not a stack of hours.
Unified data platform
One warehouse, one metrics layer, one definition of a customer — with pipelines that don't break silently overnight.
Decision dashboards
Leadership, revenue, and operational dashboards fed by governed metrics — no shadow spreadsheets, no month-end reconciliation.
Production AI assistants
Retrieval-augmented assistants over your private corpus, structured extraction pipelines, and agentic workflows with human approval gates.
Forecasting & decisioning models
Demand forecasting, risk scoring, churn prediction, and pricing models built with evals tied to business KPIs — not vanity accuracy.
Evals, observability, and cost controls
Every AI system ships with an eval suite, per-request cost telemetry, and a dashboard your team can read without us in the room.
How we deliver
The tech, kept honest.
The stack is a means, not the sell. We pick tools the team taking this over can hire for and maintain.
- Postgres / BigQuery / Snowflake warehouses
- dbt + Airflow / Dagster pipelines
- OpenAI, Anthropic, open-weight fallbacks
- pgvector / LangGraph for agentic flows
Where this shows up
Industries we run this in.
Retail & Consumer Goods
Commerce platforms, order and inventory systems, and store operations tooling that hold up through peak season.
Media & Entertainment
Content supply chains, metadata and rights systems, and streaming or publishing platforms built around audience behaviour.
SaaS & Platform
Multi-tenant architecture, usage-based billing, and enterprise-readiness work for product and platform businesses.
FAQ