The data foundation enterprise AI actually needs — unified, governed, owned by the business.
Most AI initiatives stall at the data layer. Teqfocus builds modern, governed, multi-cloud data architecture on Snowflake, Databricks, Salesforce Data Cloud, and AWS/Azure/GCP — so analytics, AI, and customer-facing systems run on one trusted truth. Strategy through operate. One partner.
The patterns we see in every stalled data program
If any of these sound familiar, the fix is rarely another tool — it's an architecture decision.
AI pilots look good in demo, fail in production
The data feeding your models is fragmented and ungoverned. The model isn't the problem — the pipeline is.
Reporting is contested at every exec meeting
Finance, sales, and ops each have their own number for the same KPI. Decisions stall or go on gut.
Data engineering is a cost center
Every new use case means a six-month build. Pipelines break on release weekends. Observability is a screenshot.
Snowflake or Databricks costs — no proven ROI
Costs climb faster than adoption. Warehouses are oversized. No FinOps discipline. Leadership is asking hard questions.
Compliance is slowing everything down
HIPAA, SOC 2, GDPR, state privacy laws — your team spends more time on access reviews than delivery. Governance was bolted on.
CRM and warehouse don't agree
Salesforce and Snowflake tell different stories about the same customer. Your Customer 360 is a slide, not a system.
We don't stop at the pipeline. We own the full data-to-decision chain.
Most enterprise data problems are architecture problems, not platform problems. Teqfocus works across the full stack — data foundation, intelligence, integration, applications — as one accountable partner.
- Technology-agnostic, outcomes-obsessedWe start with your problem, not a platform preference.
- Industry-first architectureEvery design starts with your industry's data flows and regulatory context.
- Full multi-cloud depthAWS, Azure, GCP — plus Salesforce Summit and Snowflake/Databricks Services credentials.
- Strategy through operateBlueprint, build, migrate, run. No hand-off to a separate AMS vendor.
The reference architecture
Blueprint of a High-Impact
Data Transformation Journey
Every phase of your data and AI transformation — vision to execution
Four phases, one partner. We engage where you need us most and scale up or down.
Architect
- Data strategy & maturity assessment
- Cloud readiness & migration planning
- Platform blueprint (Lake/Lakehouse/Mesh)
- Governance & data product design
- FinOps cost model
Engineer
- Pipeline engineering (batch & streaming)
- Warehouse & lakehouse build
- Real-time & event-driven architecture
- Data quality & observability
- Governance automation & lineage
Build
- Multi-cloud implementation
- Data integration & reverse ETL
- Structured + unstructured onboarding
- Semantic & metrics layer (dbt/Tableau)
- Self-service analytics enablement
Maintain
- Managed data platform services (AMS)
- Data quality ops & SLA management
- Observability & incident response
- Metadata & catalog management
- Continuous optimization & FinOps
A clear data maturity roadmap
Benchmark current state, define the target, and map a 90-day path to the next rung.
Data Aware
Data exists but siloed — limited visibility across the business.
Data Capable
Foundational systems centralize data for basic reporting.
Data Managed
Data is organized, cleansed, and governed across departments.
Data Driven
Data informs decisions, predicts trends, and optimizes performance.
Data Mastery
Data is a strategic asset driving innovation and competitive edge.
Pre-built IP that compresses months into weeks
Reusable assets that shorten every engagement.
Data Maturity Assessment
2-week benchmarking that produces current-state score, target architecture, and a 90-day execution plan.
Snowflake & Databricks Jumpstart
Production landing zone in 4-6 weeks — governance, RBAC, cost controls, observability, first use case live.
Data Cloud Onboarding Pattern
Opinionated Data Cloud build covering identity resolution, calculated insights, and Agentforce grounding.
Legacy Warehouse Migration
Teradata, Netezza, Oracle Exadata → Snowflake or Databricks. Schema translation, validation harness, parallel-run cutover.
Data Quality & Observability
Monte Carlo / Great Expectations + SLAs + data contracts — broken pipelines surface before the business feels them.
FinOps & Cost Governance
Workload-level attribution, right-sizing, autosuspend, chargeback — Snowflake and Databricks spend under control.
Your data problem isn't generic — neither is our approach
Every build starts with your industry's data flows and regulatory context.
Clinical-grade data, HIPAA-compliant by design
Unify EHR, claims, SDOH, device, and CRM data into a governed patient 360 — powering care coordination and AI-assisted clinical workflows.
- FHIR / HL7 / X12 ingestion & normalization
- Clinical data lake on Snowflake or Databricks
- PHI tokenization & access governance
- Health Cloud + Data Cloud integration
- AI for care gaps, readmission risk, acquisition
From policy admin to predictive underwriting
Connect policy, claims, billing, and third-party risk data into one carrier foundation — enabling underwriting AI and claims automation.
- Policy/claims/billing integration (Guidewire, Duck Creek)
- Predictive underwriting & risk scoring
- Claims fraud detection & SIU workflows
- FinServ Cloud + Data Cloud advisor 360
- Regulatory reporting automation (NAIC)
Product telemetry as a first-class revenue signal
Unify product usage, CRM, billing, and CS data so forecasts hold up to board scrutiny and expansion signal reaches Sales in time.
- Telemetry (Segment, Amplitude, Snowplow) into the warehouse
- Consumption & usage-based billing reconciliation
- PQL & expansion-signal scoring
- Sales Cloud + CPQ + Data Cloud integration
- Forecast & renewal-risk models with explainability
The platforms we actually deliver
Technology-agnostic in approach. Deep in every platform we recommend.
| Layer | Primary platforms | Where we use each |
|---|---|---|
| Cloud data platform | SnowflakeDatabricks Lakehouse, Redshift, BigQuery, Synapse | Enterprise warehousing, data sharing, Cortex AI, governed SQL analytics. |
| Lakehouse & ML | DatabricksDelta Lake, MLflow, Unity Catalog, Photon | Heavy-compute ML, real-time features, unstructured data, MLOps. |
| Customer data platform | Salesforce Data Cloudnative CDP on Snowflake/Databricks | Identity-resolved customer 360 for Agentforce and marketing activation. |
| Integration & pipelines | MuleSoft, Fivetran, Matillion, dbtKafka, Confluent, Airbyte, Hightouch | Batch, CDC, streaming, transformation-as-code, reverse ETL. |
| Cloud infrastructure | AWSAzure, Google Cloud Platform | AWS Advanced Partner with EKS service delivery; Azure and GCP for regulated workloads. |
| Analytics & BI | Tableau, CRM AnalyticsPower BI, Looker, Sigma | Self-service analytics, embedded Salesforce dashboards, executive reporting. |
| Governance & catalog | Unity Catalog, Snowflake HorizonCollibra, Alation, Atlan, Monte Carlo | Metadata, lineage, data contracts, observability, policy-as-code. |
| AI & intelligence layer | Databricks ML, Cortex, Agentforce, EinsteinAzure OpenAI, Bedrock, Vertex AI | Predictive models, GenAI on governed data, Agentforce grounded in Data Cloud. |
We Blend Our Data Expertise With Other Services
To Transform Your Vision Into Reality
Why data leaders choose us over the Big 4
Depth, accountability, skin in the game.
Full-stack, one throat to choke
Foundation, intelligence, integration, and application — one accountable sponsor, no vendor triangulation.
Summit + Services Partner depth
Salesforce Summit. Snowflake Services. Databricks Certified. AWS Advanced. Credentialed in every layer.
Industry specialists
Teams with working knowledge of your data model, regulations, and the questions your board will ask.
Governance from day one
Data contracts, lineage, access policy, observability — in the first sprint. Not a phase-2 afterthought.
FinOps discipline
Cost attribution, chargeback, right-sizing, autosuspend — your platform doesn't become a budget review.
Operate past go-live
Our AMS practice runs the platform after delivery — same team, same SLAs. No knowledge transfer gap.
Credentialed across every platform we deliver on
Partnerships are how we get roadmap access, pre-release enablement, and senior platform engineering on your program.
- Salesforce Summit Consulting Partner
- 1000+ Customer Success Stories
- 200+ Salesforce Certified Experts
- 100+ Industry Accelerators
- Clauude Partner Network
- Claude-powered agents in production across regulated industries
- 40+ enterprise Agentforce deployments grounded on Claude
- Teqfocus runs on Claude — we are our own first client
- AWS Advanced Consulting Partner
- 150+ Active AWS Engagements
- 50+ Skilled Practitioners & Cloud Engineers
- Data & Analytics Competency + 6 Designations
- Snowflake Services Partner
- Expertise in Data Lake to Snowflake
- 50+ Customers across key industries
- 20+ SnowPro Certified Experts
- Certified Databricks Consulting Partner
- 20+ AI & Data Workloads delivered
- 20+ Databricks-Certified Experts
- Expanding focus in Healthcare & Financial Services
Request your data maturity assessment today
Start with a 2-week Data Maturity Assessment. Leave with a scored current-state read, a target architecture, and a 90-day plan.
What leaders ask us before they sign
Direct answers.
Data transformation is rebuilding how your enterprise captures, stores, governs, and serves data so analytics, AI, and customer-facing systems run on one trusted foundation. It matters for AI because most enterprise AI initiatives don't fail on the model — they fail at the data layer. Without a modern foundation, AI outputs can't be trusted, audited, or scaled to production.
We lead with the workload, not the platform. Snowflake for governed SQL analytics, data sharing, and Cortex GenAI. Databricks for ML engineering, unstructured data, streaming, and heavy compute. Data Cloud for customer 360, Agentforce grounding, and marketing activation. Most enterprises end up with two — typically Snowflake or Databricks plus Data Cloud — and we design the architecture so data flows cleanly between them.
First production use case live in 8-14 weeks. Full platform maturity 6-12 months depending on scope. Sequence: 2 weeks for assessment and target-state architecture → 4-6 weeks for landing zone, governance, and first use case → rolling waves for additional use cases and migrations.
Most internal teams are stretched on BAU and can't prioritize strategic platform work. We accelerate what your team can't get to — modern platform build, complex migrations, AI-ready architecture, governance automation — and leave behind documentation and patterns they can maintain. The goal is acceleration, not dependency.
Governance and compliance are built into the first sprint. Our reference architecture includes tokenization, column- and row-level access, audit logging, data contracts, lineage, and policy-as-code. We deploy against your specific regulatory context — HIPAA, PCI DSS, NAIC, GDPR, state privacy laws — with your compliance team in the design sessions from day one.
Yes — common engagement. Our FinOps work covers workload-level attribution, warehouse right-sizing, query optimization, materialization strategy, and chargeback modeling. Typical outcome: 25-40% cost reduction within 90 days without reducing capability — plus governance that prevents costs from climbing again.
Yes — and it's the same team that delivered the build. Managed Data Platform services cover operations, data quality SLAs, observability, metadata management, FinOps, and new use case delivery. No knowledge transfer gap, no finger-pointing. Contracts typically 12-36 months.
Start with a Data Maturity Assessment. 2 weeks. You leave with a scored current-state read, a target-state architecture aligned to business priorities, and a 90-day execution plan — boardroom-ready whether you continue with Teqfocus or not. Book the assessment.
