AI & Intelligent Systems
Generative AI
Production RAG pipelines, fine-tuned models, and generative applications grounded in your data — governed, evaluated, and cost-controlled from the first commit.
Overview
Why it matters
Generative AI has crossed from novelty to infrastructure: enterprises now draft contracts, answer customer questions, generate code, and produce marketing at scale with LLMs. But the gap between a ChatGPT demo and a production generative system is wide. Production systems must be grounded (answers cite your documents, not the model's imagination), governed (access control, PII handling, brand and policy constraints), and measured (faithfulness and quality quantified on every release).
Our generative AI practice builds exactly that class of system. The core disciplines: retrieval-augmented generation engineered as a real information-retrieval problem — chunking strategy, hybrid dense-plus-keyword search, reranking, and metadata filtering tuned against a labeled evaluation set; fine-tuning when it earns its cost, with LoRA/QLoRA pipelines and rigorous held-out evaluation; and generation UX that presents citations, confidence, and human-review affordances rather than pretending the model is infallible.
We are equally fluent hosted and self-hosted: enterprise APIs (Azure OpenAI, Bedrock, Anthropic) where they fit, vLLM-served open-weight models inside your VPC where data residency, latency, or unit economics demand it. The recommendation always comes with a benchmark, not an opinion.
Business challenges
The problems this practice exists to solve
Hallucinations that erode trust
One confidently wrong answer in front of a customer or regulator undoes months of adoption. Ungrounded generation is a liability; grounding it properly is a retrieval engineering problem most teams underestimate.
Knowledge locked in unstructured content
Decades of contracts, reports, wikis, tickets, and recordings that keyword search can't reach. Institutional knowledge walks out the door with every departure because nothing makes it queryable.
Naive RAG that returns the wrong context
Teams stand up a vector database in a week, then discover retrieval quality is poor: bad chunking, no reranking, no metadata filtering, no evaluation set to even measure the problem.
Generation without governance
Shadow AI usage is already happening across your workforce. Without sanctioned tools that enforce access control, PII policy, and brand constraints, the risk exists anyway — just unmanaged.
Our solution
How we engineer it
We treat retrieval as the foundation, because in enterprise generative AI, answer quality is mostly retrieval quality. We build ingestion pipelines that parse your real formats (PDFs with tables, scanned documents, slide decks, HTML, transcripts), chunk them with structure awareness, and index them with hybrid search — dense embeddings plus BM25 — behind a reranking stage. Document-level permissions from source systems (SharePoint, Confluence, Google Drive) are enforced at query time, so users can only retrieve what they're already allowed to read.
Generation is then engineered for honesty: prompts constrain the model to retrieved context, answers carry inline citations, and a faithfulness-scoring pass flags responses that drift from sources. Where fine-tuning is justified — consistent structured output, brand voice at volume, small self-hosted models for cost — we run LoRA pipelines with proper held-out evaluation, so you can see precisely what the tuning bought before it ships.
Everything is wrapped in the operational layer enterprises need: evaluation suites (answer relevance, faithfulness, retrieval recall) run on every change; per-feature cost attribution with caching and model-routing to control spend; content policy enforcement; and full audit logging. The system improves measurably release over release, and your team can prove it.
Capabilities
What generative ai covers
RAG pipeline engineering
Structure-aware ingestion, hybrid retrieval with reranking, permission-aware filtering, and citation-grounded generation — tuned against a labeled evaluation set, not vibes.
Model fine-tuning & distillation
LoRA/QLoRA fine-tuning, preference optimization (DPO), and distillation of frontier-model behavior into small self-hosted models — with held-out benchmarks proving the gain.
Enterprise copilots & knowledge assistants
Grounded assistants over your document estate with source citations, role-based access inherited from source systems, and conversation analytics that show where knowledge gaps are.
Content generation systems
Template-governed generation for product content, reports, and communications — brand and compliance constraints enforced in the pipeline, human approval where policy requires it.
Multimodal AI applications
Vision-language pipelines for document understanding, image and diagram interpretation, and audio transcription-plus-synthesis workflows integrated into business processes.
GenAI governance & evaluation
Faithfulness and relevance scoring, red-team suites, PII redaction, content policy enforcement, and usage analytics — mapped to your AI governance framework and the EU AI Act.
Technology stack
Tools we deploy to production every week
Pragmatic about tools, opinionated about architecture — the platforms below are the ones this practice ships with, chosen per engagement on evidence.
Models
- GPT-5.x
- Claude
- Gemini
- Llama
- Mistral
- Qwen
Retrieval & Data
- pgvector
- Pinecone
- Weaviate
- Elasticsearch / OpenSearch
- Cohere Rerank
- Unstructured.io
Training & Serving
- Hugging Face (PEFT, TRL)
- Axolotl
- vLLM
- Ray
- AWS SageMaker
- Modal
Evaluation & Ops
- Langfuse
- Ragas
- Braintrust
- Weights & Biases
- OpenTelemetry
Implementation process
Five stages. No surprises.
A delivery model refined over 250+ engagements — sequenced so leadership gets visibility and your teams get momentum.
Use-case & content audit
We define the target user tasks, audit the source content estate (formats, quality, permissions), and build a labeled evaluation set of real questions with expert-approved answers.
Retrieval baseline
Ingestion and indexing pipeline first, measured on retrieval recall and precision against the evaluation set — because no prompt can fix retrieval that returns the wrong context.
Generation & grounding
Prompt architecture, citation formatting, faithfulness scoring, and fallback behaviors — iterated against the eval suite until relevance and faithfulness clear agreed thresholds.
Governance hardening
Permission-aware retrieval verification, PII and content-policy enforcement, red-team testing, and audit logging — signed off with your security and compliance owners.
Launch & optimize
Staged rollout with usage analytics, cost tuning via caching and model routing, content-gap reports for knowledge owners, and quarterly eval refreshes as your corpus evolves.
Use cases
Where enterprises apply it
Enterprise knowledge assistant
One grounded, citation-backed assistant over policies, product docs, and past cases — replacing keyword search that nobody trusted.
Customer support copilot
Draft answers grounded in your help center and case history, surfaced inside the agent desktop — cutting handle time while keeping humans on send.
Proposal & report generation
First drafts of RFP responses, audit reports, and account reviews assembled from your content library — structured, cited, and ready for expert editing.
Product content at scale
Descriptions, specifications, and localized variants generated under brand and accuracy constraints for catalogs of tens of thousands of SKUs.
Meeting & call intelligence
Transcription, summarization, action-item extraction, and CRM write-back for sales and service conversations — searchable institutional memory.
Code modernization assistance
LLM-assisted documentation, test generation, and translation of legacy codebases — accelerating modernization programs with engineer review in the loop.
Outcomes
Results clients report to their boards
94%
answer faithfulness score sustained in production for a financial-services knowledge assistant
37%
reduction in average support handle time after copilot rollout
8x
content production throughput for a retail catalog team, at higher consistency
62%
lower inference cost after distilling to a self-hosted fine-tuned model
FAQs
Questions leaders ask us
Direct answers on generative ai — the same ones we give in the first consultation.
Related services
Practices that pair with this one
Ready to put generative ai to work?
In a 45-minute consultation, our architects map your highest-ROI opportunity, outline a delivery plan, and give you a realistic budget range — no obligation.