HighPixel /Services /Layer 03 · Foundation /Data & MLOps
Layer 03 · Foundation

Data you
can actually trust.

From sources to warehouse to features to model serving — typed, tested, observed end-to-end. So your dashboards say the same thing as your CFO's spreadsheet, and your models keep working when reality drifts.

Engagement
10–22 weeks
audit → ship → handover
Surfaces
Warehouse · Features · Models · BI
Stack
dbt · Snowflake / BigQuery · Feast · MLflow
LIVE · pipeline state
01 · WHY DATA & MLOPS

Trust comes from the floor up

The patterns we lean on across pipelines and model serving — and why they keep dashboards honest 18 months in.

01

Most data work is plumbing

Models get the credit. Pipelines do the work. We build the boring, well-tested pipelines first — because no model survives a broken upstream join.

02

Quality is a property of the pipeline

Tests, freshness checks, schema enforcement, lineage. If a row gets to a dashboard, somebody owns it and somebody can prove it's right.

03

Models are software

Reproducible training, versioned features, shadow eval, drift monitoring. We treat models the way the platform team treats services.

02 · WHO IT'S FOR

What we ship, by the person on the hook for the data

persona

Head of Data

Pain
Three teams, three definitions of revenue. Half the dashboards disagree with the other half.
What we ship
A semantic layer over a tested warehouse, with lineage end-to-end. One definition of revenue. Owners on every metric.
persona

VP Engineering

Pain
Our 'AI' work keeps failing in production. Models work in a notebook, break in serving.
What we ship
A reproducible training pipeline, online/offline feature parity, a model registry, and shadow eval before any model touches production traffic.
persona

Chief Marketing

Pain
Our attribution is held together with hope. Reverse ETL is 3 spreadsheets and a prayer.
What we ship
A clean event spine, an attribution model the team agrees on, and reverse-ETL pipes that move trusted segments to ad / lifecycle tools.
persona

DPO / Privacy

Pain
We can't tell which datasets contain PII. We can't honor a deletion request in under 2 weeks.
What we ship
A PII catalog, automated tagging, and deletion / export pipelines. DPDP / GDPR-ready governance baked into the warehouse, not bolted on.
03 · WHAT WE BUILD

Six tracks that compose into your data foundation

We start where the trust is broken — usually pipelines and metrics — and build outward to features and models from there.

Pipelines & Warehouse

Bronze → Silver → Gold with dbt + Great Expectations. Every model tested, every dataset owned.

  • dbt + Great Expectations
  • Per-dataset SLAs
  • Lineage end-to-end

Feature Store

Online + offline parity, drift monitoring, ownership.

  • Feast or in-house
  • Online p95 12ms
  • Drift detection on every feature

Model Training & Registry

Reproducible runs, versioned features, approvable models.

  • MLflow / W&B
  • Reproducible from seed
  • Approval workflow built-in

Model Serving

Online + batch, with shadow eval and gradual rollout.

  • Online via API gateway
  • Batch via scheduled jobs
  • Shadow + canary built-in

BI & Semantic Layer

One definition of metrics, queryable from any tool.

  • dbt semantic layer
  • Metabase · Looker · Hex
  • Owners on every metric

Governance & Privacy

PII catalog, lineage, access policies, deletion pipelines.

  • DataHub · Collibra-grade lineage
  • Per-tenant access policy
  • DPDP / GDPR pipelines
04 · REFERENCE STACK

Sources → Lakehouse → Features → Serving

A reference shape we adapt to your warehouse. Sources flow in, transforms shape it, features and models live in the middle, and serving + governance sit on the right.

05 · ENGAGEMENT

From audit to durable data foundation

Most clients have a tested warehouse by week 10 and a model in production behind shadow eval by week 18.

01
Wk 1–3

Audit & metric map

We map sources, datasets, dashboards, and the metrics business owners actually use. Disagreements, gaps, and orphan pipelines get scored.

Deliverables
▸Data audit
▸Metric map
▸Pipeline RFC
02
Wk 4–10 · ACTIVE EXAMPLE

Trusted warehouse

Bronze → Silver → Gold pipelines with dbt + tests, ownership, and SLAs. The first business-critical metric becomes a single source of truth.

Deliverables
▸Tested warehouse
▸First trusted metric
▸Lineage live
03
Wk 11–18

Features & models

Feature store, training pipeline, model registry, shadow eval. The first model goes to production behind a flag.

Deliverables
▸Feature store live
▸Model in prod (shadow)
▸Drift monitoring
04
Wk 19+

Handover & rhythm

Your team owns the data foundation with a working delivery rhythm. We stay on retainer for capability expansion.

Deliverables
▸Authoring guide
▸Quarterly review
▸On-call ready
06 · THE STACK

Boring on purpose — durable for the team

We default to the tools your team will still want to use in three years.

Warehouse
Snowflake · BigQueryDatabricksRedshift (legacy)
Transforms
dbtSQLMeshGreat Expectations
Orchestration
Airflow · DagsterPrefectStep Functions
Features / ML
FeastMLflow · W&BRay · BentoML
BI
Metabase · LookerHex · Supersetdbt semantic layer
Governance
DataHub · OpenMetadataAtlanPer-tenant ACLs
07 · OUTCOMES

What changes when the data foundation is right

Aggregated across data engagements over the last 24 months.

0
Data tests passing
Median per active warehouse.
−0%
Time-to-trust on a new metric
vs. pre-platform baseline.
0ms
P95 online feature latency
At production traffic.
−0%
Model rollback rate
Shadow eval catches it before users do.
OF
OUTFITKART · ATTRIBUTION

One revenue number across product, finance, and marketing.

Read full case →

Built the event spine, the warehouse, the semantic layer. Three teams, one number. Attribution model agreed in writing — and enforced in code.

1
definition of revenue, across the company
LK
LANEKART · DEMAND ML

A demand model that survived a 6× traffic spike.

Read full case →

Reproducible training, online feature store, drift monitoring, shadow eval. When the spike came, the model degraded gracefully and self-recovered.

0
customer-visible degradations during spike
08 · FAQ

Asked on the discovery call

Probably not. Most teams need a tested warehouse and a clean event spine first. The feature store earns its place once you have 2+ models in production sharing features.

Whichever you're already on, plus a preference for the one your team will actually own. We don't pick a warehouse religion; we pick the one that lets your data team stop fighting infra and start building.

No. We pair with your team and our explicit goal is to make them stronger. Most engagements end with us as a smaller retainer doing platform work, while your team owns the analytics and modeling.

PII catalog at the column level. Per-tenant or per-region access policies. Automated deletion + export pipelines. Lineage that proves where every PII column flows.

Yes — that's the Agentic AI service area. The data foundation here is what makes those systems trustworthy. We typically run them as paired engagements.

A trusted warehouse build is typically ₹1–2.5Cr over 12–22 weeks. Add ₹50–80L for the feature store and first model in production.

Continue · related
01 · INTELLIGENCE

Decision Intelligence →

The decision layer on top of the data — experiments, optimization, what-if.
03 · FOUNDATION

Platform & Cloud →

The infra under the data foundation.
Q2 2026 · two slots open for Data & MLOps

Talk to a Data & MLOps engineer.

Bring the messy bit. We come back with an architecture sketch and a discovery plan inside two business days — no sales theatre.

response within
48h