Generative AI development
We build generative AI systems that produce work your team would otherwise do by hand (drafting documents, summarizing records, answering from your knowledge base) grounded in your data so outputs are accurate, on-brand, and auditable.
4.9 / 5 client rating
19 verified client reviews
Agentic AI Market Report 2025–2029
Recognized among notable AI engineering vendors
ISO/IEC 27001:2022
Information security management certified
AWS · Google Cloud · Microsoft
Cloud technology partnerships
Our Generative AI services
RAG knowledge systems
Retrieval-augmented generation over your documents, wikis, and databases: answers with citations, not hallucinations.
Document generation
Proposals, reports, contracts, and correspondence drafted from your templates and live data, ready for human review.
Summarization pipelines
Long records (calls, cases, threads, filings) condensed into structured summaries your team actually reads.
Fine-tuning & model adaptation
When prompting isn't enough, we adapt models to your domain language and formats with fine-tuning and structured evaluation.
LLM evaluation & safety
Automated eval suites that score accuracy, tone, and policy compliance before and after every change.
What we build with it
Internal knowledge assistants
Staff ask questions in plain language; the system answers from your own documentation with sources.
Content operations
Product descriptions, listings, and marketing variants generated at scale under editorial control.
Intelligent document processing
Unstructured PDFs, emails, and scans turned into clean, structured data in your systems.
Meeting & call intelligence
Transcripts turned into action items, CRM updates, and follow-up drafts automatically.
Where this delivers the most
A six-step path from idea to production
Frame the problem
We start with your business goal, not the technology. Workshops with your team surface the workflows, bottlenecks, and metrics that matter.
Assess feasibility & ROI
Every candidate use case gets a feasibility check and a dollar figure: expected savings or revenue against build and run cost.
Prototype fast
A working proof of concept on your real data within weeks, so decisions are made on evidence instead of slideware.
Build for production
Security, permissions, monitoring, and human-in-the-loop controls are engineered in from the start, not bolted on later.
Integrate & launch
We connect the solution to the tools your team already uses, train your people, and launch with clear rollback plans.
Measure & improve
Post-launch, we track the metrics defined in step one and keep tuning (models, prompts, and workflows) as your business evolves.
The stack behind the work
Chosen per project for capability, cost, and your team's ability to own it later, never by vendor allegiance.
- Claude (Anthropic)
- GPT (OpenAI)
- Gemini
- Llama
- Mistral
- LangChain
- LlamaIndex
- Hugging Face
- PyTorch
- TensorFlow
Common questions
Related work
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An in-house legal team reviewed hundreds of vendor agreements a year against the same playbook. A review workspace we designed and built flags deviations, cites clauses, and drafts fallback language: lawyers make the calls.
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A multi-clinic healthcare network was losing revenue to claim denials that staff had no time to analyze or appeal. We automated denial triage and appeal drafting, and built the analytics to stop denials at the source.
Read case studyTell us about your project
We'll come back within one business day with an honest read on feasibility, approach, and cost.