Gen AI That Survives Contact With Production
Generative AI consulting for the enterprise — RAG platforms, domain assistants, and agentic workflows with the governance to pass legal, safety, and regulatory review. Built on Databricks Mosaic AI, Azure OpenAI, Vertex AI, and Claude.
Why Generative AI Programs Stall
Most enterprise Gen AI stalls in one of two places: the pilot that never leaves the sandbox because legal and quality review can't approve what nobody can trace, or the assistant that ships and quietly erodes trust with confident, unsourced answers. The gap between a demo and a production system is governance, grounding, and evaluation — the unglamorous parts.
The Apptad Approach to Generative AI
We build Gen AI systems that are grounded, sourced, and auditable from day one: governed corpora, retrieval architectures tuned to your domain, evaluation harnesses that quantify quality before users find the failures, and the safety review artifacts your compliance function needs to say yes.
Grounded on governed data
RAG over Unity-Catalog-governed corpora with full source traceability — every answer cites, every citation resolves.
Evaluation before rollout
Domain-expert-validated eval sets and regression harnesses — quality is a measured number across releases, not a vibe.
Safety review as a deliverable
Our top-10 pharma assistant passed GxP validation and FDA-acceptable digital systems review — because the audit trail was designed in, not retrofitted.
Agentic where it earns it
Multi-step agents for workflows with checkable outcomes; simple retrieval where that’s what the job needs. Architecture follows the task.
Generative AI in Production
Top-10 Pharma Cuts Clinical Trial Prep 50% with LLM-Powered Research Assistant
Domain-tuned LLM on Databricks Mosaic AI accelerates literature review, protocol drafting, and inclusion-criteria modeling.
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Tier-1 Carrier Cuts Churn 14% with GenAI Customer Intelligence
Vertex AI + BigQuery customer intelligence platform combines churn prediction with GenAI-powered next-best-action for 38M subscribers.
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Commercial Bank Cuts Credit-Memo Turnaround 58% with Claude-Powered Document Agents
Agentic document review on Anthropic Claude — every extracted figure cited back to its source page — took middle-market credit memos from eleven days to under five, with the model-risk audit trail designed in.
Read the case studyGenerative AI, Answered Straight
How do you stop an LLM assistant from hallucinating?
You constrain and verify rather than hope: retrieval-grounded generation over a governed corpus, mandatory source citation, evaluation sets that measure faithfulness, and domain-expert review loops. Our pharma research assistant produces fully sourced output — that's why it passed safety review where a previous pilot failed.
Which foundation model should we standardize on?
Usually none — you standardize on the platform and evaluation harness, and let the model be swappable. Model rankings shift quarterly; a grounded architecture with measured quality lets you change models in days. We build on Databricks Mosaic AI, Azure OpenAI, Vertex, and Claude depending on your cloud gravity.
What makes Gen AI projects fail regulatory or legal review?
Untraceable training/grounding data, no audit trail on outputs, and no quantified evaluation. All three are architectural choices. Design them in from the start and review becomes a checklist; retrofit them and you're rebuilding the system.
Part of our AI / ML practice.
Apptad rebuilt our fraud-detection stack on Databricks lakehouse from the ground up. Sub-200ms decisioning, eight-figure annual loss avoided, and audit trails the regulators actually praised. A rare combination.
Have a Generative AI Question? Ask the People Who Ship It
Our AI / ML practice is led hands-on. Connect on LinkedIn or send a note through the form — a practitioner (not a sales rep) replies within one business day.
