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Agents that do the work, not just talk about it

A chatbot answers questions. An agent queries your systems, decides, executes the action, and knows when to ask a human for approval.

2,000,000 active users served · 10 countries in operation

  • LangGraph
  • Multi-agent orchestration
  • Tool use
  • MCP
  • Human-in-the-loop
  • Traceability
Contact us See the process Free, no pitch · the proposal lands in 48 hours

Built on

  • Anthropic
  • Claude
  • Google Gemini
  • Google Cloud
  • LangChain
  • LangGraph
  • PostgreSQL
  • Qdrant
  • Datadog

An engineering team that has already been on the other side.

Ninety seconds: who we are, how we work and what you get at the end.

From workflow to agent in production

01

Map the real workflow

With the person who runs it today, not with the manual. What we are looking for are the exceptions: the odd case someone resolves with a phone call, the one that skips a step, the one nobody ever wrote down. That is where a badly built agent breaks.

02

Design the graph and the guardrails

Which steps are automatic, which need approval, which tools get exposed and with what permissions. Agreed before any code is written.

03

Build with evaluation running

A set of real cases with expected outcomes, running from day one. Every change gets compared against the previous run.

04

Scoped pilot

Released against a real subset of the work, supervised and measured. It expands when the numbers justify it, not when the demo impresses.

05

Production and observability

Step-level traces, failure and cost alerts, and a dashboard of what the agent did. Delivered documented.

01

Types of agent

What we build

01 4–6 weeks

Single agent

One concrete workflow end to end: classifying and routing requests, preparing quotes, reconciling documents, following up on a process.

02 8–12 weeks

Multi-agent systems

Several specialized agents that coordinate and hand work to each other, with an orchestrator deciding who does what and when to escalate.

03 6–10 weeks

Internal copilots

An agent inside the tool your team already uses, with operational context and role-based permissions.

04 2–4 weeks

Custom MCP servers

We expose your systems as standardized tools through Model Context Protocol, so any agent can use them without brittle one-off integrations.

What separates an agent from a demo

Five things almost nobody implements

  • Durable state.

    The agent survives restarts, resumes where it stopped and does not repeat actions already executed. Without this, any failure means redoing the work from scratch.

  • Human in the loop.

    Actions that move money, write to systems of record or talk to a customer go through configurable approval.

  • Tool boundaries.

    Each agent reaches only what its task requires, with role-based permissions and tenant isolation.

  • Retries and replay.

    When a tool fails the agent retries sensibly; when something goes wrong the run can be replayed step by step to understand what happened.

  • Cost and step limits.

    An agent without a ceiling loops and burns budget. Ours all carry step, time and spend caps.

This is where the data comes in

An AI system is worth what its sources are worth. These are the standard connectors; anything with an API or a database connects the same way, and what has no API is handled by file.

SAP

Enterprise ERP

ERP

Oracle

ERP and database

ERP

NetSuite

Cloud ERP

ERP

Salesforce

CRM and service

CRM

HubSpot

CRM and marketing

CRM

PostgreSQL

Database and pgvector

Databases

Fourteen sectors, and in each one the measurement changes

Insurance

Policy wordings, exclusions, claims

Says what a policy covers, quoting the clause and the version in force that day. If the wording changed in March, it answers with March’s.

AI for Insurance →

An example

— Am I covered if my car is stolen from a public car park?

Yes, with a 10% deductible. 2026 policy wording, clause 4.3.

A RAG system in production: three years, two million users

We built and operated the assistant for a loyalty platform serving more than two million active users across ten countries.

We built the full pipeline: document ingestion and normalization, chunking, embedding generation, vector store on Azure AI Search and Pinecone, and retrieval with grounded generation on LangChain.

We held it above 99% availability for three years.

RAG systems →

Frequently asked questions

01

What is the difference between an agent and a chatbot?

A chatbot converses: it takes a question and returns text. An agent executes: it takes a goal, decides which steps to take, uses tools to query or modify your systems, evaluates the result and corrects. A chatbot tells you an order’s status; an agent notices the order is late, notifies the customer, reschedules the shipment and logs all of it.

02

Which framework do you use and why?

LangGraph for most production cases, because it models the flow as a graph with durable state, allows pausing for human approval, retrying a single step and replaying a full run for debugging. For simple flows n8n with model calls is sometimes enough, and that is what we propose.

03

What if the agent does something wrong?

It depends how wrong. Sensitive actions — those that move money, write to systems of record or talk to a customer — go through configurable human approval. The rest is bounded by permissions and by step, time and cost limits. And everything lands in an audit trail, so when something goes wrong you can reconstruct exactly what happened.

04

Does it connect to our CRM and ERP?

Yes, as long as they expose an API or an accessible database. We have integrated inventory systems, payment platforms, CRMs and in-house databases. When several systems are involved we usually expose them as MCP servers, which standardizes access and prevents every new agent from needing bespoke integrations.

05

What does it cost to run an agent per month?

It depends on volume and how many steps each run takes. For a mid-market company it usually lands between USD 150 and 900 per month in model and infrastructure consumption. We estimate it with concrete numbers in the proposal, and task-based routing exists precisely to keep that figure low.

06

Does it replace people?

In practice, almost never. The agent takes the mechanical work — classifying, searching, copying between systems, following up — and the person keeps what requires judgment, which is usually where the bottleneck was. If your goal is different and you do want to shrink the team, say so on the first call: it changes what has to be documented and how much the agent has to handle unsupervised.

07

How long until the first agent is in production?

Four to six weeks for a single workflow, counting from when we have access to the information and systems. The prior exploration takes one to two weeks more and exists so we do not build on assumptions.

08

Can we start small?

That is what we recommend. The exploration reviews one concrete workflow, evaluates whether an agent adds value, and delivers the design with its estimate. If we proceed, it is credited against the project. It is the cheapest way to find out whether this makes sense in your operation before committing a quarter.

Fifteen minutes. A concrete answer.

It gets fixed, it gets built, or it is not worth it. And if the diagnostic does not reach three actionable findings, it is not charged.

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