Generative AI that earns its keep
in production.
Custom LLM applications, RAG systems, copilots, and content engines grounded in your data. Shipped with evaluation harnesses, cost tuning, and the compliance layer your legal team actually signs.
Past the demo, into the workflow.
Generative AI demos well and ships poorly. The gap is engineering — retrieval, evaluation, cost control, and integration into real product surfaces. We build for the second part. If you're still exploring the capability space, start with our broader AI and ML services overview.
Every answer traces back to your corpus. Citations, confidence thresholds, and fallback paths are first-class — not afterthoughts.
Prompt architecture, function calling, structured outputs, and fine-tuning where prompting caps out. The system gets better as your data grows.
Monitoring, rollback, cost dashboards, and regression suites come with the deployment. AI isn't done when it's live — it's done when it's operable.
Six disciplines, one delivery contract.
Pick one, combine several — most engagements mix custom applications with RAG and fine-tuning. We scope the composition in discovery so the architecture matches the ambition.
- 01
Strategy and transformation roadmap
Turn board-level ambition into a sequenced plan. We audit where generative AI compounds value in your operations, score candidate use cases, and ship a 12-month roadmap you can fund.
- 02
Custom LLM applications
Purpose-built applications on top of GPT, Claude, Gemini, Llama, and open-weight models. Function calling, tool use, structured output — engineered for your product surface, not a generic playground.
- 03
RAG and knowledge systems
Retrieval-augmented generation grounded in your documents, tickets, and proprietary corpus. Semantic search, reranking, citation-first answers — so the output is defensible, not hallucinated.
- 04
Content generation engines
Product descriptions, marketing copy, structured reports, code — generated at scale with guardrails, brand-voice tuning, and human-review checkpoints where the stakes demand them.
- 05
Model fine-tuning and distillation
When prompting hits its ceiling, we fine-tune. Instruction tuning, preference alignment, and model distillation that cut inference cost 5–10x without sacrificing quality on your task.
- 06
AI-agent and orchestration
Multi-agent workflows, autonomous reasoning loops, and complex tool-use orchestration. We build the logic that lets AI take actions, interact with your existing APIs, and solve multi-step problems autonomously.
Where generative AI is already paying.
These aren't speculative — they're documented deployments. For deeper teardowns, browse our case study archive.
E-commerce catalog at scale
A client generated 50,000+ product descriptions across 100,000+ SKUs in six months — 50% less manual effort, 20% higher conversion, 10% more organic traffic, and $150,000 in annual savings.
Manufacturing demand forecasting
Generative models paired with historical demand signals cut inventory holding costs 10%, improved forecast accuracy 15%, reduced waste 20%, and shortened lead times 30%.
Enterprise support copilots
LLM-backed support copilots trained on your product documentation, historical tickets, and resolution patterns — deflecting the repetitive layer so humans focus on the hard cases.
Healthcare intake and triage
Clinical assistants that structure patient notes, suggest ICD-10 codes, and draft discharge summaries — all with clinician-in-the-loop review to preserve accuracy and compliance.
Financial research automation
Earnings-call summarization, KPI extraction from 10-Ks, and multi-document comparison — the analyst gets the synthesis in minutes and spends the day on judgment, not assembly.
Marketing personalization
Generate landing-page, email, and ad variants per segment — even per user — with brand guardrails and performance feedback loops that retrain the system weekly.
Four verticals, specific playbooks.
The generative AI stack is general. The playbook that makes it land in your industry isn't. These four are where we've shipped the most — but we move outside them when the brief fits. See our full industries directory.
E-commerce
Product content, search, merchandising, personalized recommendations.
Healthcare
Clinical notes, medical imaging reports, patient intake, compliance-aware summarization.
Finance
Research automation, fraud investigation assistants, document-heavy underwriting.
Marketing
Campaign generation, creative variation, brand-aligned content at segment scale.
Two production systems, documented outcomes.
Real clients, measured impact. Both are adaptable as enterprise engagements sized to your data and compliance envelope.
Catalog copy for 100,000+ SKUs, generated in six months instead of three years.
Demand forecasting that cut inventory, waste, and lead times at once.
The difference between a demo and a durable system.
Anyone can wire an LLM to a prompt in a day. Shipping a generative system that performs six months in, at scale, with auditable outputs — that takes a different discipline.
Grounded, not guessing
Every generative system we ship is grounded — RAG, tool use, or fine-tuning — so outputs trace back to your source of truth. Hallucination isn't a feature to live with; it's an architecture problem to solve.
Cost-tuned by default
We architect for the price/quality frontier. Prompt caching, model routing, distillation, and hybrid retrieval cut your cost per call by 3–10x without users noticing a drop.
Brand-aligned output
Every LLM system ships with tone, terminology, and policy guardrails trained into the pipeline — not bolted on as a moderation layer that humans have to babysit.
Evaluation as first-class
Production AI needs regression suites, not vibe checks. We ship evaluation harnesses, golden test sets, and drift alerts so quality is measurable, not anecdotal.
Model-agnostic architecture
OpenAI, Anthropic, Google, or open-weight — your stack stays portable. When the frontier shifts, you switch providers in a config file, not a rebuild.
Compliance-aware from day one
PII handling, audit trails, output filtering, and regional routing are designed in — so your legal and security teams sign off without a scramble at the end.
What teams ask before they build.
01When should we build with generative AI vs. classical ML?
02How do you prevent hallucinations in production?
03What does a fine-tuning project look like?
04How do you keep sensitive data out of third-party LLMs?
05Can generative AI work on proprietary data our legal team won't upload?
06What does a generative AI engagement cost?
07Who owns the prompts, training data, and deployed model?
Your generative AI, in production.
One call, a scoped POC on your real data, and a working system in two to four weeks. From there, production if the economics and accuracy clear the bar.