ESTalk to our team

AI that plugs into the way your company already works.

Six things we build for companies, each connected to your CRM, ERP, documents and internal tools. Every one comes with a person in the loop, a test it must pass, and a boundary on where your data goes.

Ask for a demoSee the six offers
Agent run
  1. Read the order from the CRMcrm.read
  2. Check stock in the ERPerp.read
  3. Draft the customer replydraft
  4. Issue the credit noteWaits for a person
Sample data

An agent on its own is a demo.

Connected through MCP to your CRM, ERP and documents, it starts doing the work.

With scoped keys, approvals and a log, you can let it.

Drag it, or use the arrow keys.

AI agentMCPCRMERPDocuments

Six offers, one way of working

AI agent development

Agents that carry a multi-step task through your systems: read the request, look things up, decide within limits, prepare the action.

What your team gets
A process that no longer needs someone copying between tools, and a readable log of every step the agent took.
When it fits
Judgment-heavy work with unstructured input that crosses several systems, such as support triage, order exceptions or back-office requests.
How it’s evaluated and kept safe
Tool access limited to what the job needs. A person approves anything irreversible. A set of known cases must pass before any change ships.
Plan an agent for your process
Illustration of a small robot greeting with a wave.
Agents that work inside your software. Illustration

MCP servers for products

An MCP server gives Claude, ChatGPT, Cursor or any MCP client a controlled way to use your product: searching, drafting, acting through tools you define.

What your team gets
A product that agents can operate, for your staff and your customers, without giving anyone the keys to the database.
When it fits
You sell or run software with an API, and your users already want their AI tools to work with it.
How it’s evaluated and kept safe
Scoped keys decide what each agent may do. Risky tools can require approval. Every call is recorded with its key as the actor.
Scope an MCP server for your product
MCP server for your product
  • orders.searchread
  • customers.getread
  • invoice.draftwrite
  • invoice.issueapproval

Key scope decides what each agent may call. Every call is logged.

Sample data

MCP is the door. You decide who gets through.

One server in front of your systems, one set of scoped keys, one log. Add a system and every authorized agent can use it.

Internal RAG

Retrieval over your own contracts, policies, tickets and wikis, so a model answers from your documents and cites them.

What your team gets
One place to ask, answers with their source one click away, and fewer interruptions for the people who know everything.
When it fits
Knowledge scattered across PDFs, wikis and email, with the same questions asked again and again.
How it’s evaluated and kept safe
Retrieval respects who may read what. Answers cite sources. Quality is scored on questions with known answers, and the index stays in your infrastructure when it must.
Connect your documents
Internal assistant

What is our return window for B2B orders?

30 days from delivery, with a note from the account owner.

  • Sales policy, section 4
  • Contract template B2B
Only documents you may read
Sample data

LLM providers and local models

Deployment and integration with LLM providers, or local models running on your own servers, chosen per task.

What your team gets
One interface over several models, so you can change vendor, cut cost or move a workload in-house without rewriting your application.
When it fits
You have data that must stay put, costs growing with volume, or a need not to depend on one provider.
How it’s evaluated and kept safe
Data classes decide which model may see what. Each model is scored on your tasks before it’s used, not on public benchmarks.
Plan your model deployment
Model routing
  • Customer contractsLocal model, your servers
  • Support repliesLLM provider
  • Bulk classificationSmaller model
Sample data

Task automation

Repetitive work across tools: intake, sorting, data entry, document processing and follow-ups, using rules where rules will do and AI only where judgment is needed.

What your team gets
Hours back, fewer dropped requests, and workflows you can read, in custom code or n8n and similar tools.
When it fits
High volume and a clear definition of done. If a plain script would work, we write the plain script.
How it’s evaluated and kept safe
Low-confidence items go to a person’s queue. Failures retry, then raise an alert. Nothing fails silently.
Show us the task to automate
Approval flow
Sample data

Assistant creation

Assistants for staff or customers, inside your product, your intranet or the tools people already open.

What your team gets
Answers where the work happens, in your language and your tone, handing off to a person when they should.
When it fits
People repeatedly need information or small actions from a system that is hard to use or hard to reach.
How it’s evaluated and kept safe
Answers come from approved sources. Actions ask first, or stay read-only when the data calls for it. Conversations are reviewed against agreed criteria.
Design your assistant
Assistant over your data

Which orders haven’t moved in over three days?

I found 6. Four are waiting on purchasing and two need a billing detail.

  • Order log
  • Purchasing inbox
Ask your data
Sample data

What a scoped key looks like

This is the ladder in Wordex Agentic, our CMS that doubles as an MCP server. Give each agent the lowest rung its job needs.

See Wordex Agentic

  1. readList and search.
  2. previewSee drafts.
  3. writeCreate and change drafts.
  4. publishPublish or schedule.
  5. adminChange the structure.

What we connect it to

AI pays off when it can read and act where your work already happens. We integrate through APIs, MCP servers, webhooks and queues.

Map your integrations
  • CRM: account context for every reply, updates written back, follow-ups created
  • ERP: orders, stock and invoices read in real time, with approval before anything is posted
  • Documents: contracts, policies and PDFs, respecting who may read what
  • Internal tools: tickets, chat and the spreadsheets nobody has replaced yet
  • Your own product: an MCP server and assistants your customers can use

Three things every project has

Human approval

You choose which actions wait for a person. Agents propose, and irreversible steps stop until someone says yes.

Evaluation

Before building, we write down what correct means, with real cases. A change that doesn’t pass doesn’t ship.

Data where it must stay

Data classes, permissions and model choice decide what leaves your servers. Local models when nothing should.

Classic machine learning and deep learning too

Not everything needs an LLM. We also offer classic machine learning for forecasting and classification, and deep learning for text, images and sequences. A model can stand on its own, or become one more tool your agents call.

See Data Science & Predictive Analytics

Talk about a model

See the variants in our own products

We build AI into our products in the form each one needs. Ask us to show you any of them.

QuorikAn agent with 16 tools whose every change is a proposal a person applies or discards.Wordex AgenticA CMS that is also an MCP server, operated through scoped keys.FinZenA copilot that cites its numbers and asks before acting, plus an MCP server.ChimeraAn MCP server and an agent checkout, with people deciding what changes in the store.RoutingExA routing engine with an MCP server and an AI assistant.ElderCareA read-only assistant over MCP that never changes a record.
Ask for a demo

How we run agents in our own work

We use what we sell. This is how our work runs today.

Engineering
Claude Code CLI with our own skills, Engram for persistent memory, gentle-ai for orchestration, and MCP servers for access to tools. OpenAI and Gemini Antigravity are part of the work too.
Company operations
Paperclip for virtual teams, OpenAI Symphony for tickets, Hermes and OpenClaw for day-to-day automations, agents for lead capture, and n8n for workflows.
Models
Frontier models from Claude, OpenAI, Gemini and DeepSeek. Local models, Qwen and Gemma, for automation that stays on our own machines.
MCP servers we built
An Illustrator MCP server that runs a design workflow with stops for approval, and a video workflow from idea to delivery.

Before you ask

Which systems can you connect to?

Anything with an API, a database or a file export, and often things without one, through a thin service we add. Tell us what you run.

Do we need clean data first?

Not all of it. We start with the data the first task needs. If it’s too inconsistent to use, data engineering becomes the first step, and we’ll say so.

How do we know it works?

We agree on test cases with your team before building, then score against them on every change. You see the score.

Can we see a demo?

Yes. We give demos on request using the products and materials we built.

Which process should an agent take off your team’s desk?

Tell us the process and the systems it touches. We’ll show you something similar running and tell you what we’d connect first.

Ask for a demo