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Data, Analytics & AI
Generative AI Solutions
RAG, copilots, grounded LLMs.
Generative AI engineered into production — retrieval-augmented architectures, fine-tuned models, and copilot patterns grounded in your governed enterprise knowledge.
The case
Generative AI is
an engineering problem.
Most enterprise GenAI failures aren't model failures. They're retrieval failures, eval failures, governance failures, integration failures. The model picks the words; the system picks whether the words are right, safe, and worth shipping.
We design GenAI as an engineered system — grounded retrieval, structured eval, observable behaviour, and the same release discipline you'd apply to any high-stakes platform. The model is one component. The architecture around it is the product.
If your copilot can't show its work, it isn't enterprise-ready. It's a demo.
Our framework
Four pillars
for a copilot you can defend.
Grounded
Every answer is traceable to a source the user can open. No source, no answer.
Bounded
Permissions, redaction, and content policy enforced before retrieval — not as a trailing safeguard.
Evaluated
Continuous eval suites that catch hallucination, regression, and prompt drift before users do.
Observable
Every prompt, retrieval, and generation is logged and replayable. Audit trails are first-class.
What we build
From RAG fundamentals
to fine-tuned copilots.
RAG architectures
Production-grade retrieval — chunking strategy, reranking, evaluation, and grounded citations users can verify.
Fine-tuning & alignment
Domain fine-tunes (LoRA, QLoRA, full) with eval suites, safety alignment, and guardrails specific to your operating risk.
Copilot patterns
Grounded copilots embedded in CRM, underwriting, claims, knowledge, and developer workflows — not standalone chat surfaces.
Eval & observability
LLM evaluation harnesses, hallucination scoring, prompt-drift detection, and continuous monitoring in production.
Vector & knowledge platforms
Engineered retrieval substrate — hybrid search, reranking, freshness, and multi-tenant isolation.
Safety & compliance layer
Content policy, PII redaction, jailbreak resistance, and regulator-ready audit trails engineered in.
Reference architecture
The RAG stack,
engineered in layers.
We treat the retrieval substrate, the model layer, and the safety plane as separate concerns — each independently testable and replaceable.
Knowledge plane
Layer 01Source-of-truth content with freshness and provenance.
Retrieval substrate
Layer 02Hybrid search, reranking, and tenant isolation.
Model layer
Layer 03Foundation-aware, fine-tune-aware, multi-vendor.
Safety plane
Layer 04Policy enforcement before, during, and after generation.
Eval & observability
Layer 05Continuous testing, replay debugging, hallucination scoring.
Outcomes we engineer for
What grounded GenAI
actually moves.
−68%
Cycle time
Median quote-to-bind cycle reduction at a Tier-1 carrier when the underwriter copilot is grounded properly.
<2s
P95 latency
Typical end-to-end latency on a hardened RAG stack with hybrid retrieval and rerank.
100%
Citation coverage
Every response is traceable to a source. No source — no response.
Zero
Silent regressions
When eval is wired into CI, prompt and model regressions surface before they reach users.
Where this applies
Wherever expertise
is locked in documents.
GenAI fits anywhere the work is reading, drafting, summarizing, or reasoning over structured knowledge — which is most knowledge work, in most industries.
- Insurance & Reinsurance
- Banking & Capital Markets
- Wealth Management
- Legal & Professional Services
- Healthcare Providers
- Pharma & Life Sciences
- Public Sector
- Higher Education
- Manufacturing
- Energy & Utilities
- Telecom & Media
- Retail
- B2B SaaS
- Defense & Aerospace
Common questions
What teams ask
before greenlighting.
Do we need to fine-tune our own model?
Often no. Most enterprise GenAI value comes from retrieval, eval, and engineering discipline — not from fine-tuning. Fine-tuning is a tool, not a goal.
How do we keep this safe and compliant?
Safety is an architectural concern, not a feature. We engineer the safety plane — redaction, policy, audit, replay — alongside retrieval and generation, not bolted on later.
Will this lock us into one model vendor?
No. We engineer routing and fallback so the model layer is replaceable. Most clients run multi-vendor by month six.
How do you measure if it's actually working?
Decision-first eval. Every copilot has a measurable workflow KPI — cycle time, deflection, satisfaction — and a continuous eval suite that catches regressions before users do.
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Strategy & Consulting
Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.
Data Lakehouse & Warehouse
Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.
Advanced AI & Analytics
Forecasting, segmentation, and anomaly detection wired into operational workflows.
Start the conversation
From the demo
to a copilot you'd defend in audit.
Most clients start with one workflow, ship in 8–14 weeks, and expand from there. We'll help you find the right one.