The Altimate platform at work.

Altimate agents build pipelines from a plain-language spec. They scan your existing schema and lineage, identify reusable models, generate dbt SQL with the correct grain and joins, write targeted tests based on column contracts and business rules, and produce documentation from context.

Every generated pipeline passes blast radius analysis before it ships. Your data engineers review and approve — they don't build from scratch.

Every workload migration has the same hurdles: the business logic embedded in legacy Informatica mappings, SSIS packages, or SQL scripts — documented nowhere, understood by engineers who may no longer be at the company.

Altimate extracts that business logic before migration begins. Decision Memory pulls Git history, PR comments, and architectural context into ADR-formatted traces. The migration agent uses this context to produce dbt models that preserve compliance rules, business semantics, and data contracts — not just schema structure.

One customer migrated 400+ Informatica pipelines in under three weeks, with the business logic intact and contracts validated. The initial estimate was 12 weeks.

Building a data app from scratch usually means three separate workstreams: a data engineer building the pipeline, a backend engineer writing the API, and a frontend engineer building the interface. Coordination overhead alone can double the timeline.

Altimate agents generate the full stack from a plain-language spec. They scan your existing semantic layer to reuse models where possible, generate the API layer, and produce frontend components wired to the correct data sources. Auth and IAM configuration is inherited from your existing setup.

Every generated app passes blast radius analysis. The data engineering, backend, and frontend output is reviewable before deployment — engineers merge, not rebuild.

Altimate agents continuously monitor warehouse health, trace anomalies to root cause using historical incident patterns, and surface blast radius instantly: which downstream models, dashboards, and PII columns are affected. Root cause is surfaced with a confidence score. The agent then resolves autonomously at 95%+ confidence, or escalates with a full context packet.

The engineers who know which Snowflake or Databricks queries are expensive are the same engineers shipping features. Nobody has time to audit 3,000 queries, so the bill grows.

Altimate's cost intelligence is built on 3 years of Fortune 500 query patterns. The agent scans your warehouse, identifies optimization opportunities, calculates blast radius for every proposed change, and implements with a full audit trail. Auto-Tune handles warehouses autonomously. Query optimization works cross-platform.

One customer ran 168 query optimizations, achieving $8M in annual savings. Another deployed Auto-tune in under 3 weeks and saved $1.8M a year.

Documentation debt and missing tests are the hidden tax on every data team. They slow onboarding, break agents, and turn compliance reviews into archaeological digs. Most tools generate documentation from schema alone — which tells you column names, not what they mean or why they're structured the way they are.

Altimate generates documentation from context — pulling from column lineage, business semantics, PII classification, and ownership data. The dbt Power User extension (500K+ downloads, 30K weekly active users) has been doing this in VS Code for three years.

Agents also generate targeted dbt tests before any model change ships — not generic schema tests, but assertions derived from the business logic in the model. Data quality becomes a pre-condition for agent autonomy, not a post-incident cleanup.