Production AI infrastructure that is measured before it is promoted.

Model pipelines, evaluation, deployment, and observability for ML and LLM workloads — with governance built in rather than bolted on.

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mlops / pipeline · support-assistant v14running
6 steps · 1 human gate0 / 6
01 / Problem → solution

Where teams get stuck, and how we engineer past it.

THE PROBLEM
  • 01Models and prompts reach production without repeatable evaluation.
  • 02Quality, latency, and cost of AI features are not visible after launch.
  • 03Pipelines are notebooks and scripts that only one person can run.
OUR APPROACH
  • 01Reproducible pipelines for data, training or retrieval, evaluation, and release.
  • 02Evaluation gates that block regressions before promotion.
  • 03Tracing and cost attribution for every model call in production.
02 / What we build

What a mlops engagement delivers.

01

Pipelines

Versioned data, feature, and embedding pipelines.

02

Evaluation

Offline suites and online checks for quality and safety.

03

Model serving

Deployment patterns from shadow to canary to live.

04

Vector systems

Retrieval infrastructure with permission-aware indexing.

05

Observability

Latency, drift, quality, and cost dashboards with alerting.

06

Governance

Model registry, lineage, and approval records.

TECHNOLOGY
  • MLflow
  • Databricks
  • Kafka
  • PostgreSQL
  • Vector databases
  • Prometheus
  • Grafana
03 / Representative solutions
REPRESENTATIVE SOLUTION

AI observability platform

06 / 06
PROBLEM
LLM features in production lacked visibility into quality, latency, and cost.
ARCHITECTURE
Tracing for every model call, evaluation pipelines, and cost attribution by feature.
WHAT WE BUILT
Dashboards, regression evaluations in CI, and alerting on quality drift.
BUSINESS OUTCOME
AI behavior that can be measured, compared, and governed.
TECHNOLOGYOpenTelemetryMLflowPrometheusGrafana
REPRESENTATIVE SOLUTION

Enterprise knowledge assistant

03 / 06
PROBLEM
Teams spent hours searching across wikis, tickets, and documents for operational answers.
ARCHITECTURE
Permission-aware retrieval over company sources, grounded generation, and citations on every answer.
WHAT WE BUILT
An assistant in Slack and on the web that respects existing document permissions.
BUSINESS OUTCOME
Faster answers grounded in company sources, with access controls preserved.
TECHNOLOGYAnthropicVector databasePostgreSQLSlack
04 / More solutions

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