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Feature stores
Tecton, Feast, or custom implementations on Snowflake / Databricks. Feature parity between training and serving with shared semantics, lineage, and governance. Eliminates the 'works on my laptop' class of model failures.
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AI & Machine Learning · CORTEX
MLOps & AI Infrastructure.
The substrate production AI actually rests on — feature stores, model registries, eval harnesses, deployment pipelines, A/B testing, drift monitoring, governance evidence. Built once, reused across every AI use case the org will ship in the next two years.
- Practice
- AI & Machine Learning
- Department
- CORTEX
The problem
Most enterprises have AI models in production and no operating substrate underneath.
The shape across enterprise AI estates: every team that shipped a model has its own feature pipeline, its own eval set (if any), its own deployment process, its own monitoring (if any). Three teams compute the same customer-LTV feature with three subtly different definitions; the model registry is a Notion page; promotions to production happen via Slack DM and a manual deploy. The first model ships through heroics; the second through more heroics; the third stalls.
Prosigns ships the MLOps substrate that makes the third, fourth, tenth model cheap to deploy. Feature stores with feature parity training ↔ serving. Model registries with full lineage. CI/CD pipelines that run eval gates and drift checks before deployment. A/B testing infrastructure for production model comparison. Governance evidence — model cards, audit logs, validation reports — produced as a side-effect of operating the system.
Where it ships
6 use cases, in production.
Specific applications we’ve built and operated. Not speculative — every example below is grounded in a real shipped engagement.
- 02
8x
deploy frequency
Model registry + deployment pipelines
Centralized model registry with version control, lineage, and approval gates. CI/CD pipelines that run eval suites, drift checks, and equity audits before promoting to production. Rollback automated on regression.
- 03
Eval harnesses at scale
Standardized eval infrastructure across the AI estate — every model evaluated against curated ground-truth datasets in CI, with per-subgroup stratification where equity matters. Eval becomes a release gate, not a dashboard.
- 04
Drift monitoring + retraining cadences
Production drift monitoring against eval datasets, not training datasets. Retraining cadences calibrated per workload (weekly to quarterly); active-learning loops feed borderline cases back to labeling.
- 05
A/B testing infrastructure
Production model A/B with explicit statistical-significance gates, equity-aware subgroup analysis, and rollout playbooks. Compare frontier-vs-mid-tier LLMs on real workload, not generic benchmarks.
- 06
Governance evidence pipelines
Model cards, audit logs, validation reports, lineage records — produced continuously, not assembled at audit time. SR 11-7 (financial services), HIPAA (healthcare), 21 CFR Part 11 (life sciences) frames supported.
How we engage
4 phases, named in the SOW.
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans — operating rules.
- 01
Substrate audit
Two-week audit of the existing AI estate: model inventory, feature pipelines, eval discipline, deployment process, monitoring posture, governance frame. Output is honest assessment with prioritized remediation roadmap.
- 02
Foundation build with first use case
Stand up the substrate alongside a real production AI use case — feature store, model registry, deployment pipeline, eval harness, drift monitoring. The use case proves the substrate works; subsequent models inherit it without re-engineering.
- 03
Governance frame from day one
Model cards, audit logging, validation reports, lineage tracking — designed in, not retrofitted. SR 11-7 / HIPAA / 21 CFR Part 11 frames mapped to architectural components for engagements that require them.
- 04
Operating cadence
Quarterly model reviews against the eval set. Monthly drift triage. Weekly status to engineering leadership. Hand off to your team with the runbook, or stay engaged under Managed Services for ongoing AI ops.
Capabilities
What’s in scope.
- Feature stores: Tecton, Feast, custom implementations on Snowflake / Databricks
- Model registries: MLflow, Weights & Biases, custom with lineage tracking
- CI/CD for ML: deployment pipelines with eval gates and drift checks
- Eval harness infrastructure: standardized across the AI estate
- Drift monitoring: continuous, equity-aware, retraining-trigger integration
- A/B testing: production model comparison with statistical-significance gates
- Governance: model cards, audit logs, validation reports, SR 11-7 / HIPAA
- On-call / SRE for production AI workloads
Stack
Tools we use in production.
- Feature store
- TectonFeastDatabricks Feature StoreAWS SageMaker Feature Store
- Model platform
- MLflowWeights & BiasesCometBentoMLRay
- Deployment
- AWS BedrockAzure OpenAIVertex AIvLLMTGI
- Eval
- PromptfooRagasDeepEvalLangSmithCustom harnesses
- Observability
- LangSmithHeliconeLangfuseDatadog LLMArize
- Streaming + data
- Apache KafkaAWS KinesisSnowflakeDatabricksdbt
Selected work
Quantified outcomes, not adjectives.
Common questions
Asked before the first call.
- 01
Why a feature store specifically?
Without a feature store, three teams shipping the same customer-LTV feature will compute it three subtly different ways — and at least one of those will diverge between training and serving. Feature stores enforce parity, lineage, and shared semantics. They're not the most exciting part of the substrate, but they're the part that determines whether your models work in production.
- 02
Do we need this if we only have one model in production?
Probably not yet. MLOps substrate becomes load-bearing somewhere between the second and fourth production model — when the cost of duplicated infrastructure exceeds the cost of building the substrate. Below that, lighter-weight tooling (MLflow + a few CI pipelines) is sufficient. We tell you when you've crossed the threshold.
- 03
Can you ship MLOps on top of our existing data platform?
Yes — and we usually do. Snowflake, Databricks, BigQuery, and on-prem warehouses all support modern feature stores. We assess the substrate during discovery and recommend the smallest architectural change that unblocks production AI. Greenfield rebuilds are rare; most engagements layer MLOps on existing data infrastructure.
- 04
How do you handle SR 11-7 / regulated AI governance?
Documentation produced continuously, not at audit time. Model cards generated from registry metadata; audit logs cover input / output / model version / user identity per inference; validation reports map to SR 11-7 sections (or HIPAA, 21 CFR Part 11 for other regulated workloads). Second-line review co-authored rather than retrofit.
- 05
Can you operate the platform after we ship it?
Yes — through Managed Services. Production drift monitoring, retraining cadences, quarterly model upgrades, eval dataset maintenance with SMEs. Or we hand off to your team with the runbook and a 90-day shadow period.
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What does an MLOps engagement cost?
Substrate audit: 2–4 weeks, $40K–$120K. Foundation build + first use case: 6–10 months, $600K–$2M. Multi-use-case enterprise programs: $2M–$6M. Managed Services for ongoing AI ops: $40K–$200K monthly retainer.
Within AI & Machine Learning
Other capabilities in this practice.
Talk to us
Bring a mlops & ai infrastructure problem. We’ll bring a senior engineer.
A senior engineer plus the CORTEX department lead joins the first call. No discovery gauntlet, no junior reps.