AI integration into existing products
Embed models, retrieval, and automation into the web apps, ERP systems, and portals your team already operates.
Learn moreApplied AI
Production intelligence, not slide decksAI that solves real problems — not demos.
We integrate models, agents, and automation into the software and operations you already run — with evaluation, human checkpoints, and a clear path when things go wrong.
Discuss an AI projectSee our workProduction AI architecture: business trigger through agent orchestration, retrieval with vector database context, evaluation, human checkpoint, and production delivery.
Business problems
Where AI removes friction in day-to-day operations.
Each card names a business situation we have built for — not a generic capability list.
- 01
Inbound leads sit in the ERP system for hours before anyone calls back.
An AI outreach agent scores intent, runs first contact, and books qualified meetings into your calendar.
Prospects hear back in minutes. Sales reps spend time on conversations that can close.
- 02
Sales orders arrive as emails and PDFs that someone re-types into the ERP.
Document extraction, stock and pricing validation, and automated sales order creation with an exception queue.
Orders land in the system in minutes instead of the next morning.
- 03
Support tickets pile up while agents search three systems for the right answer.
Classification, retrieval from your docs, and a draft response your team approves before it goes out.
First responses go out faster without sacrificing accuracy.
- 04
Invoices and purchase orders need manual matching before finance can post them.
Field extraction, PO matching, and posting to your accounting system — with a review lane for mismatches.
Routine documents clear automatically. Exceptions get a named owner.
- 05
Company knowledge lives in Slack threads, PDFs, and people's heads.
A retrieval layer over your approved sources with citations and access controls.
Teams find answers in one place instead of asking the same person again.
- 06
Customers call to ask where their order is because status is buried in the ERP.
A status agent connected to order data that answers common questions and escalates edge cases.
Fewer status calls. Support handles exceptions, not repeat lookups.
- 07
Quality review means someone opening every file and checking a long checklist.
Model-assisted screening that flags items for human review based on your criteria.
Reviewers focus on the 10% that actually needs judgment.
- 08
Weekly reports are assembled by hand from five spreadsheets every Friday.
Scheduled extraction, aggregation, and narrative summaries sent to the right channel.
Reports arrive on time with less manual copying and fewer transcription errors.
Workflow examples
Follow the workflow from trigger to outcome.
Select a scenario. The diagram shows how data moves; the steps explain it in plain language.
Leads get a response in minutes, not days.
- 01
A new lead enters the ERP system from a form, ad, or import.
- 02
The AI agent places an outbound call or sends a chat message with context from the lead record.
- 03
The conversation is transcribed and scored for intent, budget fit, and timeline.
- 04
Qualified leads get a meeting booked and a warm handoff note for the rep.
- 05
Unqualified leads enter a nurture sequence instead of clogging the pipeline.
- 06
A human rep takes the close — the agent handled speed and qualification.
Multi-agent orchestration
Agents that coordinate — with humans in the loop.
One orchestrator routes work to specialist agents. Validation and human checkpoints sit before anything reaches production.
- 01
Request arrives
A user question, document, or system event enters the orchestrator with context and access scope.
- 02
Orchestrator routes work
The orchestrator decides which specialist agents to invoke and in what order — not every request needs every agent.
- 03
Retrieval agent
Pulls relevant passages from approved knowledge sources with citations the user can verify.
- 04
Tools and APIs agent
Calls external systems — ERP, ticketing — with scoped credentials and rate limits.
- 05
Validation agent
Checks outputs against policy rules, format constraints, and confidence thresholds before anything ships.
- 06
Human checkpoint
High-stakes or low-confidence results pause for human approval before the customer or system sees them.
- 07
Evaluation loop
Outcomes feed back into test sets and monitoring so quality drift is caught before users report it.
Model Context Protocol
Power your existing system with MCP.
The Model Context Protocol lets Claude, ChatGPT, Gemini, and other MCP clients read and act on live business data — without replacing the ERP system or databases you already operate. We design and build the MCP server.
- 01
Ask the chat you already use
Live orders, inventory, tickets, and docs in Claude, ChatGPT, or Gemini — instead of exporting CSVs and pasting them into a thread.
- 02
Take actions with a checkpoint
Create a record, update a ticket, or trigger a workflow. High-stakes calls still pause for a human before anything commits.
- 03
Build once, connect many clients
One MCP server for Claude Desktop and chat, ChatGPT custom connectors over HTTPS, Gemini CLI, Cursor, GitHub Copilot, and VS Code.
- 04
Keep auth and audit
Scoped tools, credentials that never live in the prompt, and a trail of what the model was allowed to do.
Works with the clients your team already uses
- Claude
- ChatGPT
- Google Gemini
- Cursor
- GitHub Copilot
AI services
AI services from integration through custom model work.
Start where the risk is highest — we do not sell a fixed AI product stack.
Custom AI/ML model development
Fine-tuned models, classification pipelines, and domain-specific training when off-the-shelf models are not enough.
AI agents and multi-agent orchestration
Specialist agents coordinated by an orchestrator — with tool access, memory boundaries, and human checkpoints.
RAG and knowledge systems
Retrieval over your documents, wikis, and databases with access controls and citation-backed answers.
Workflow automation
n8n and custom pipelines that connect the tools your business runs on — with visible steps and failure alerts.
Evaluation, guardrails, and monitoring
Test sets, regression checks, cost tracking, and alerting so production AI does not drift unnoticed.
MCP development
Custom MCP servers for your ERP system and internal APIs so Claude, ChatGPT, and Gemini can use the systems you already run.
Learn moreEngagement process
How an AI engagement runs from first conversation to production.
- 01
Frame the decision
Name the workflow, the user, and what a good outcome looks like — before choosing a model or framework.
- 02
Feasibility spike
A short, bounded experiment on real data to test whether the approach works before committing to a build.
- 03
Evaluate against real cases
Golden test sets, edge cases, and human review loops that catch failures before customers do.
- 04
Integrate with oversight
Ship into your product or operations with checkpoints, logging, and rollback paths built in.
- 05
Operate and improve
Monitor cost, latency, and quality. Feed production outcomes back into evaluation so the system gets better over time.
Technology stack
Models, orchestration, and data — chosen for the workflow.
Models, orchestration, databases, and automation — selected for fit, not fashion.
Explore all technologies- Node.jsAPIs, integrations, and event-driven services.
- SupabasePostgres-backed product foundations.
- PostgreSQLRelational foundations for operational products.
- RedisFast working memory for queues, cache, and session state.
- pgvectorVector similarity search inside PostgreSQL.
- PineconeManaged vector database for production retrieval.
- OpenAIEvaluated AI product capabilities and automation.
- Anthropic ClaudeReasoning and knowledge workflows with explicit controls.
- n8nAccountable automation across the tools a team already uses.
- LangChainComposable retrieval and tool-using model workflows.
Common questions
Working with AonHive on AI.
01Do you build chatbots or production AI systems?
Both — but we start with the workflow, not the interface. A chatbot is one surface. We also build document routers, classification pipelines, agent orchestration, and automation behind existing products.
02How do you handle AI failures in production?
Every system we ship has a defined failure path: fallback responses, human escalation, logging, and evaluation loops. We design for the case where the model is wrong — because it will be, sometimes.
03Can you work with our existing ERP system?
Yes. Most AI work connects to systems you already run — Salesforce, custom ERPs, ticketing tools, accounting platforms. We scope integrations as part of the workflow design.
04How long does a first AI integration take?
A feasibility spike can run in two to four weeks. A production integration depends on data readiness, integration complexity, and evaluation requirements — we scope the first milestone in the initial discussion.
05What is MCP (Model Context Protocol)?
MCP is an open standard that lets AI clients call tools and read data from your systems through a single server. Instead of building a separate integration for every chat product, you expose scoped tools once — query orders, search docs, create tickets — and connect the clients your team already uses.
06Can we use MCP in Claude, ChatGPT, and Gemini?
Yes. Claude Desktop and Claude chat connect to local or remote MCP servers. ChatGPT custom connectors use remote HTTPS endpoints. Gemini CLI and many developer tools — Cursor, GitHub Copilot, VS Code — also speak MCP. We build the server; you connect the clients you already pay for.
Applied AI
Bring the workflow that needs to run better.
Tell us the workflow, the decision it should improve, and what success looks like. We'll map a starting path — with evaluation and oversight from day one.