Enterprise AI that actually ships, scales, and audits — not another stalled pilot.
Most AI programs fail between proof-of-concept and production.
The patterns we see in every stalled AI program
If any of these sound familiar, the fix is rarely another model — it's architecture, data, and operating discipline.
Pilots look good in demo, never reach production
POCs stall in the gap between data science and platform engineering.
Agents hallucinate — the business won't trust them
Agentforce or a copilot was launched without identity-resolved, governed data underneath.
GenAI spend is climbing, ROI isn't
Model API bills, vector infra, and vendor licenses compound. No workload-level attribution.
The AI roadmap is a list of tools, not a plan
Bedrock, OpenAI, Vertex, Einstein, Cortex — every group bought something different. Nothing composes.
Responsible AI and compliance are blocking launch
Legal, risk, and audit want explainability, red-teaming, and policy — your team is still figuring out where to put it.
Models drift — nobody notices until the business does
Deployed models are running without monitoring, feedback loops, or retraining cadence.
AI only works if the foundation underneath does — we own both.
Most enterprise AI problems are data-and-architecture problems wearing a model badge.
- Grounded by defaultEvery agent, copilot, and model ships on identity-resolved data, not vibes.
- Responsible AI built inPolicy, red-teaming, explainability, and observability are day-one, not phase-two.
- Platform-agnosticDatabricks, Cortex, Agentforce, Bedrock, Azure OpenAI, Vertex — we pick per workload, not per vendor.
- Strategy through operateFrom use-case roadmap to live agent ops — same team, no hand-off.
The reference AI architecture
Blueprint of a High-Impact
AI Transformation Journey
Every phase of your AI transformation — vision to execution
Four phases, one partner.
Architect
- AI/ML strategy & use-case roadmap
- GenAI & Agentforce readiness assessment
- Responsible AI & governance design
- Reference architecture & platform selection
- Business case & value engineering
Engineer
- ML & deep learning model development
- MLOps setup (MLflow, Unity Catalog, CI/CD)
- Vector DBs & retrieval pipelines
- Model assurance, evaluation & red-teaming
- Feature store & real-time features
Build
- Agentforce industry agents
- Copilots, chatbots & RAG systems
- Process automation & AI agents
- LLM deployment (Bedrock / Azure OpenAI / Vertex)
- Einstein & Cortex activation
Operate
- Model & agent monitoring
- Feedback loops & human-in-the-loop
- Fine-tuning & retraining cadence
- Cost optimization (tokens, GPUs, inference)
- AIOps for AI systems
A clear AI maturity roadmap
Benchmark where you are. Define where you're headed.
AI Curious
Exploring pilots, fragmented adoption, minimal business impact.
AI Enabled
Point AI tools deployed for specific functions.
AI Integrated
AI embedded across workflows on AI-ready data pipelines — cross-functional adoption.
AI Driven
AI powers prediction, personalization, and enterprise-wide automation with responsible governance.
AI Transformative
AI anchors new products, services, and revenue streams.
The AI platforms we actually deliver
Technology-agnostic in approach.
| Layer | Primary platforms | Where we use each |
|---|---|---|
| Enterprise agents | AgentforceEinstein Copilot, industry agent templates | CRM-native agents for service, sales, field, and industry workflows — grounded in Data Cloud. |
| Foundation & GenAI models | Azure OpenAI, Bedrock, Vertex AIAnthropic, Mistral, Meta Llama, open-weights | Model selection per workload — cost, latency, accuracy, sovereignty, privacy. |
| ML engineering & MLOps | Databricks MLMLflow, Unity Catalog, Feature Store | Model training, tracking, registry, deployment, and lineage for production ML. |
| In-warehouse AI | Snowflake CortexCortex Analyst, Cortex Search, native functions | GenAI on governed data without moving it — LLM-powered queries, search, and summarization. |
| Retrieval & vector | Vector DBsPinecone, Weaviate, pgvector, OpenSearch, Databricks VS | RAG systems grounded in enterprise knowledge — with lineage and access control. |
| Customer data for AI | Salesforce Data Cloudnative CDP on Snowflake/Databricks | Identity-resolved customer 360 as the grounding layer for Agentforce and marketing AI. |
| Responsible AI & evaluation | MLflow Evaluate, Unity CatalogLangSmith, evals, red-team frameworks | Accuracy, toxicity, bias, drift — governance gates before and after deployment. |
| AI applications & BI | Einstein, Tableau Pulse, CRM Analyticscustom copilots, LangChain, Streamlit | AI surfaced where users actually work — Salesforce, dashboards, internal tools. |
Your AI use cases aren't generic — neither is our approach
Every build starts with your industry's workflows, data flows, and regulatory context.
Clinical-grade AI, HIPAA-compliant by design
Ground clinical agents and predictive models in unified EHR, claims, SDOH, and device data
- Care-gap and readmission-risk models
- Prior-auth & UM automation agents
- HEDIS / Star / HCC predictive scoring
- Clinical summarization & ambient documentation
- PHI-safe RAG & explainable outputs
From document chaos to underwriting AI
Cut policy processing, claims triage, and underwriting cycle times with document AI, agents, and predictive risk models
- Document AI for submissions, endorsements, claims
- Predictive underwriting & portfolio risk
- Claims fraud & SIU agents
- Guidewire / Duck Creek + AI orchestration
- Explainability & audit trail by default
AI that compounds GTM and CS efficiency
Ship revenue-moving AI
- PQL & expansion-signal scoring
- Renewal-risk & forecast models with explainability
- Service deflection agents on Agentforce
- Support case summarization & routing
- Product copilots on usage + CRM data
TeqAgent — the AgentOps layer that keeps Agentforce in production.
Every AI program eventually hits the same wall: silent config failures, invisible quality drift, and zero institutional memory.
Catches silent config failures
A mismatched @InvocableVariable broken tool, or schema drift rejects sessions before execution.
Scores quality drift in plain English
A dual-path judge reads what the agent actually said and scores faithfulness, tool choice, trajectory, and goal success — at the turn and session level.
Builds institutional memory
Every case arrives with a matched playbook — pattern, root cause, recommended fix, and the historical success rate of that fix. The library compounds.
Pre-built IP that compresses months into weeks
Reusable assets that shorten every engagement.
AI Readiness Assessment
2-week benchmarking that scores current maturity, identifies high-ROI use cases, and delivers a 90-day execution plan.
Agentforce Industry Agents
Pre-built agent patterns for healthcare, insurance, and SaaS — grounded in Data Cloud, deployable in 6–8 weeks.
Document AI Pack
Reusable pipeline for extracting, classifying, and reasoning over enterprise documents — insurance, clinical, contracts.
RAG Reference Architecture
Opinionated RAG stack with vector store, retrieval evals, access control, and citation — ready to deploy on your cloud.
MLOps Landing Zone
Production MLflow + Unity Catalog + model registry in 4 weeks — CI/CD, monitoring, evaluation, drift detection.
Responsible AI Playbook
Policy, red-team patterns, bias & toxicity evals, human-in-the-loop gates — co-delivered with your legal and risk teams.
We Blend Our Data Expertise With Other Services
To Transform Your Vision Into Reality
Why AI leaders choose us over the Big 4
Depth, accountability, skin in the game.
Full-stack, one throat to choke
Data, MLOps, models, agents — one accountable sponsor, no vendor triangulation between data team, ML team, and app team.
Summit + Services Partner depth
Salesforce Summit. Snowflake Services. Databricks Certified. AWS Advanced.
Industry specialists
Teams with working knowledge of your workflows, regulations, and the questions your CRO, CMO, and CRO will ask.
Grounded, not guessing
Every agent and model ships on identity-resolved, governed data.
AI FinOps discipline
Workload-level attribution for tokens, GPUs, and inference.
Operate past go-live
Our AIOps practice runs the agents and models after delivery — drift, evals, retraining, cost.
Credentialed across every platform AI runs on
Partnerships are how we get roadmap access, pre-release enablement, and senior platform engineering on your program.
- Salesforce Summit Consulting Partner
- 1,000+ 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
Book your AI readiness assessment
Start with a 2-week AI Readiness Assessment.
What leaders ask us before they sign
Direct answers.
AI transformation is the end-to-end capability to ship, scale, and govern AI — not a one-off demo. It spans strategy, data foundation, MLOps, model engineering, agent applications, and operations. A pilot proves a point.
We lead with the workload, not the platform. Agentforce for CRM-native agents grounded in Data Cloud. Databricks ML for heavy-compute training, MLOps, and unstructured data. Cortex for GenAI directly on governed Snowflake data.
First production use case live in 8–14 weeks. Broader AI maturity 6–12 months.
Most internal AI teams are strong on modeling but stretched on platform, productionization, and governance. We accelerate the gap between a notebook and a live agent — MLOps, grounding, evals, compliance, integration — and leave behind documentation and patterns your team can extend.
Responsible AI is built into the first sprint. Grounding, retrieval, citation, red-teaming, bias and toxicity evals, human-in-the-loop gates, and audit logging are reference-architecture defaults.
Yes — common engagement. AI FinOps covers token & GPU attribution, model selection per workload, caching and retrieval tuning, prompt and context-window optimization, and chargeback modeling.
Yes — and it's the same team that delivered the build. AIOps covers model and agent monitoring, drift detection, evaluation gates, retraining cadence, and cost governance, plus new use-case delivery. No knowledge transfer gap, no finger-pointing.
Start with an AI Readiness Assessment. 2 weeks. You leave with a scored maturity read, a prioritized use-case portfolio tied to business value, a reference architecture, and a 90-day execution plan — boardroom-ready whether you continue with Teqfocus or not.
