Build AI Systems · 03

Generative AI

Retrieval-grounded copilots and document AI — answers with citations, not guesses.

How grounded AI works

Every answer traced to a source.

Query
Retrieve
your knowledge
Ground
in context
Generate
Cite
every claim
Grounded answer

Yes — the policy allows it up to the stated limit, and the exception process applies for anything above it.

policy-handbook.pdf · p.14exceptions-matrix.xlsx
What we build

Generative AI, grounded in your world.

For teams buried in documents and repetitive questions who have been burned by chatbots that answer confidently and wrongly.

RAG systems

Assistants grounded strictly in your current, owned sources — with provenance.

Document AI

Extraction, classification, and packet assembly from messy documents.

Custom copilots

In-workflow assistants wired to your tools and data.

Knowledge & search

Semantic search over your corpus, every answer traceable to a source.

Fine-tuning & adaptation

Where a base model genuinely needs your voice or domain.

Grounded evaluation

Factuality, citation coverage, and refusal quality — measured, not assumed.

Services

Five ways we deliver generative AI.

From finding the right use cases to governing models in production, these are the service lines we bring — end to end, or wherever you need us.

GenAI consulting

We find the high-value use cases, define the value metrics, and build a phased adoption roadmap tuned to your functional priorities — plus ideation and enablement to bring your teams along.

Use-case discoveryValue metricsRoadmap

GenAI implementation

We move from idea to production with domain-specific language models, pre-built accelerators, and custom solutions integrated cleanly into your enterprise systems.

DSLMsAcceleratorsCustom builds

Data infrastructure for GenAI

We lay the groundwork — pipelines, data quality, governance, and a readiness framework — so models tap trusted, well-governed, business-critical data.

PipelinesQualityReadiness

GenAI governance & LLMOps

A unified layer for single-pane visibility, cost control, and safety — an LLM gateway and vetted model garden, evaluation, versioning, and lineage across the lifecycle.

LLM gatewayCost controlLLMOps

Multimodal GenAI

Solutions that span text, image, audio, and video — from document intelligence and creative generation to voice — wherever the content lives.

Text & imageAudio & videoDocument AI
Workflows

Every generative-AI workflow, mapped.

The patterns and architectures we build with — from grounding answers in your knowledge to governing many models behind a single, cost-controlled gateway.

1 · Retrieval-augmented generation (RAG)

Your documents are indexed once; every question is answered against them live — retrieved, reranked, grounded, and cited — so the model answers from your knowledge instead of its guesses.

Indexing · offline
Your documents
Chunk
Embed
Vector store
Query · live
Question
Embed
Retrieve + rerank
LLM — grounded
Answer + citations
2 · The three-layer GenAI stack

Enterprise GenAI matures in layers: ground answers first with retrieval, add context with memory and tools, then let agents execute autonomously — each layer adds capability and needs governance to match.

Layer 1 — Retrieval (RAG)
ground answers in your knowledge, with citations
Layer 2 — Context
memory, tools, and orchestration around the model
Layer 3 — Agentic execution
autonomous, governed action across your systems
3 · GenAI adoption lifecycle

We take a use case from idea to scale on a disciplined path — prioritized by value and feasibility, proven in a prototype, evaluated honestly, hardened and governed, then deployed and operated.

Discover
Prioritize
value × feasibility
Prototype
Evaluate
Harden & govern
Deploy
Scale & operate
4 · Choosing the build approach

There is a spectrum from prompting to a purpose-built domain model. We pick the lightest approach that clears the bar — and combine them when it helps — rather than defaulting to the most expensive one.

Prompting
Fastest. General tasks, no private data needed.
RAG
Ground in your knowledge. Answers that cite sources.
Fine-tuning
Teach format, tone, or a narrow skill from examples.
Domain-specific LLM
Deep domain fluency where general models fall short.
5 · LLM gateway & governance

Every app, agent, and copilot talks to models through one governed gateway — routing, guardrails, PII redaction, caching, and cost control in one place — backed by a vetted model garden and full observability.

Apps
Agents
Copilots
LLM gateway
routing · guardrails
PII redaction · caching
rate & cost control · logging
Model garden
multiple vetted LLMs
Observability
evaluation · audit
6 · Multimodal pipeline

Not everything is text. We build pipelines that take documents, images, audio, and video, reason over them together, and produce the answer, summary, or generated media you need.

Text
Image
Audio
Video
Multimodal model
understands & reasons across inputs
Answers & summaries
Generated media
In production

Generative AI at work across your business.

Domain-aware GenAI applications that automate operations, sharpen decisions, and deliver measurable return. A sample of what we build, by function.

Marketing
Use case

Creative generation

Generate on-brand copy, creative assets, and personalized messaging across digital channels.

Use case

Content at scale

Produce and localize campaign content across products and markets, fast.

Use case

Personalized recommendations

Tailor product and content recommendations to each customer in real time.

Sales
Use case

Sales enablement assistant

Synthesizes product knowledge and past interactions to answer buyer questions quickly.

Use case

Relationship-manager co-pilot

Drafts briefs, follow-ups, and research summaries to lift RM productivity.

Use case

Proposal & RFP drafting

Assembles tailored proposals grounded strictly in your approved content.

Knowledge & documents
Use case

Document intelligence

Extract, summarize, and answer across contracts, records, and reports at scale.

Use case

Research summarization

Read and summarize large volumes of research and technical documents.

Use case

Contract analysis

Surface clauses, obligations, and risks across large sets of agreements.

Customer support
Use case

Grounded support assistant

Answers from your knowledge base and live systems, with citations.

Use case

Email triage & response

Categorizes, prioritizes, and drafts replies to inbound email.

Use case

Agent assist

Real-time suggestions and call summaries for human agents on live cases.

Compliance & risk
Use case

Regulatory summarization

Condense lengthy filings and policies into concise, actionable insight.

Use case

Marketing compliance review

Check promotional content against brand and regulatory rules before launch.

Use case

Exception prediction

Predict and resolve transaction or process exceptions before they escalate.

Engineering & R&D
Use case

Design document intelligence

Extract insight from engineering files, tests, and specs to speed validation.

Use case

Code assistant

Explain, generate, and review code grounded in your own repositories.

Use case

Knowledge search

Semantic search across R&D and design history to accelerate the work.

FAQ

Questions teams ask.

How do you stop it from hallucinating?
We ground the system strictly in retrieved, current sources and require citations on every claim. When retrieval confidence is low, the system says so and hands off — and we measure factuality and citation coverage continuously.
Will our data be used to train a model?
No. Your data grounds the assistant's answers and is never used to train foundation models. We build to your data-handling and compliance requirements.
Do you fine-tune or use retrieval?
Usually retrieval first — it's cheaper, more current, and easier to audit. We fine-tune only when a base model genuinely needs your voice or a specialized domain, and we'll tell you honestly which one fits.
Get started

Bring us a hypothesis. Leave with a system.

Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.