Custom AI solutions,
shaped around
the way your business works.
Off-the-shelf tools can be useful, but they rarely understand the decisions, systems and customer expectations that make your business different. We design and implement practical AI solutions around your real workflows, from the first discovery conversation through to a system your team can use with confidence.
Useful AI starts with the work already happening inside your business.
AI becomes valuable when it removes friction from a real process. That may mean helping a support team respond with better context, giving staff a faster way to find information, or turning a repetitive administrative task into a controlled workflow. Our custom AI services connect the technology to the work, rather than asking the work to adapt to a fashionable tool.
Define the point where time, uncertainty or manual effort is costing your business. A focused starting point makes it easier to test the idea and judge whether it deserves further investment.
The useful part of an AI solution is rarely the prediction or generated answer on its own. We map the inputs, approvals, exceptions, integrations and human checks that make the process dependable.
Agree what better looks like before implementation begins. Depending on the use case, that may include faster handling, more consistent information, fewer handovers, better customer experiences or clearer operational decisions.
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.
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.
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.
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.
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.
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.
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.
Q
When
should we build with generative AI vs. classical ML?
Q
How
do you prevent hallucinations in production?
Q
What
does a fine-tuning project look like?
Q
How
do you keep sensitive data out of third-party LLMs?
Q
Can
generative AI work on proprietary data our legal team won't upload?
Q
What
does a generative AI engagement cost?
Q
Who
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.