ERP.ioAI ERP with agents working inside the systems you run.
VB.coOperating AI™ — one intelligent layer for middle-market ops.
LLM.coPrivate, self-hosted LLMs & custom AI applications.
LAW.coSecure legal AI infrastructure for firms & legal teams.
Automatic.coAI-powered workflow & business process automation.
RFP.coAI RFP discovery & automated proposal responses.
RMA.ai
Search.co
Phony.ai
VDR.aiCustom AI applications, end to end.
Customer-facing AI products, internal copilots, AI-native SaaS. We design, build, and deploy AI applications that ship — and stay shipped — with the architecture, observability, and ownership you'd expect from software you'd actually rely on.
Five categories of AI applications we build.
Chat & assistants
Conversational interfaces grounded in your data, brand voice, and product context.
Copilots & in-product AI
AI features inside an existing product surface, accelerating the user's primary task.
AI search & discovery
Semantic, citation-grounded search across your content corpus.
Agents & autonomous workflows
Multi-step systems that complete operational work end to end.
Generative tools
Product features that produce content — text, image, code, structured data — on demand.
Embedded AI features
AI capabilities added to your existing product. Lowest scope, often highest impact.
Most AI apps are made of these building blocks.
A typical project picks 2–4 and combines them. We have dedicated pages on each.
Retrieval (RAG)
Grounds answers in your documents, with citations and evals.
Agents
Multi-step systems that use tools to complete work autonomously.
Private LLMs
Self-hosted models for sovereignty, compliance, or cost.
Workflow automation
Operational automations with humans in the loop.
Vector & search
Semantic search, hybrid retrieval, citation grounding.
Document intelligence
Parsing, OCR, extraction from messy real-world documents.
From idea to live AI app in six steps.
Discover
Use case audit, success criteria, model selection, build-vs-buy assessment.
Design
Architecture, data flow, UX, evaluation framework, security posture.
Prototype
Working spike on real data within 2–4 weeks. Validates the hardest unknown first.
Evaluate
Golden dataset, eval harness, A/B testing, human-in-the-loop QA.
Deploy
Production rollout with observability, rollback plan, monitoring dashboards.
Scale
Continuous optimization, fine-tuning, capability expansion, model migrations.
How AI application projects engage with us.
- Use case audit + architecture
- Working prototype on real data
- Cost/timeline model + go/no-go
- Full architecture across all layers
- Eval harness + integrations
- Phased rollout + 30-day support
- Quarterly model migration evals
- Prompt + eval iterations + new capabilities
- On-call + monthly reports
Common questions.
AI app vs. regular app with AI features?
Hosted models or private LLMs?
How long does a build take?
Will I own the code?
How do you handle evals?
What about hallucinations?
Related reading
Agents need tools before they can do anything useful.
Tell us about the AI app you want to build.
A 30-minute call. We'll talk through what you're trying to accomplish, what's been tried, and what the right next step is — Discovery, Prototype, or straight to Production.