Custom AI Integration & Agentic Workflows
RAG, multi-agent systems
TL;DR
We integrate LLMs and agentic workflows into your operations, retrieval-augmented (RAG) assistants and multi-agent systems that cut hours and de-risk decisions.
Overview
The demo that wins the meeting is easy. The system that survives a regulated production environment is not, and that difference is almost entirely engineering: grounding, guardrails, access control and auditability.
We integrate LLMs and agentic workflows directly into your operations. Retrieval-augmented assistants answer from your proprietary knowledge with inline citations and calibrated abstention; multi-agent systems orchestrate multi-step work with guardrails and human-in-the-loop where it matters.
We deploy inside your boundary where the data demands it, instrument the baseline before launch, and report the delta, hours returned and risk reduced, because enterprise AI has to justify itself in numbers.
Who it’s for
- Enterprises with deep proprietary knowledge trapped in documents
- Teams losing hours to repetitive, judgement-heavy workflows
- Regulated environments that need grounded, auditable AI
Outcomes
- RAG over your proprietary knowledge
- Multi-agent orchestration with guardrails
- Measurable hours saved and risk reduced
How we work
AI readiness assessment
We map high-value use cases, data readiness and risk, and define the baseline to measure against.
Grounded prototype
A RAG or agentic prototype on your real data, with citations and guardrails, to prove value fast.
Production hardening
Access control, audit logging, abstention and evaluation, deployed in your boundary.
Measure & expand
We measure hours saved and risk reduced, then expand to the next workflow.
What you get
- A grounded RAG assistant or multi-agent workflow
- Inline citations, guardrails and calibrated abstention
- Role-aware access control and full audit logging
- A measured baseline and ongoing evaluation harness
Questions
How do you stop it from hallucinating?
Every answer is built from retrieved passages and renders inline citations; the system is tuned to abstain when confidence is low rather than guess. We treat un-cited answers as defects.
Does our data leave our environment?
Not unless you want it to. We deploy inside your VPC with role-aware access and audit logging, so proprietary knowledge stays within your boundary.
How do we know it is worth it?
We instrument the baseline before launch and report the measurable delta, hours returned and risk reduced, so the investment is justified in numbers.
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