Single agent
One concrete workflow end to end: classifying and routing requests, preparing quotes, reconciling documents, following up on a process.
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
Built on
Ninety seconds: who we are, how we work and what you get at the end.
01
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
Which steps are automatic, which need approval, which tools get exposed and with what permissions. Agreed before any code is written.
03
A set of real cases with expected outcomes, running from day one. Every change gets compared against the previous run.
04
Released against a real subset of the work, supervised and measured. It expands when the numbers justify it, not when the demo impresses.
05
Step-level traces, failure and cost alerts, and a dashboard of what the agent did. Delivered documented.
01
One concrete workflow end to end: classifying and routing requests, preparing quotes, reconciling documents, following up on a process.
Several specialized agents that coordinate and hand work to each other, with an orchestrator deciding who does what and when to escalate.
An agent inside the tool your team already uses, with operational context and role-based permissions.
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
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.
Actions that move money, write to systems of record or talk to a customer go through configurable approval.
Each agent reaches only what its task requires, with role-based permissions and tenant isolation.
When a tool fails the agent retries sensibly; when something goes wrong the run can be replayed step by step to understand what happened.
An agent without a ceiling loops and burns budget. Ours all carry step, time and spend caps.
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
Oracle
ERP and database
NetSuite
Cloud ERP
Salesforce
CRM and service
HubSpot
CRM and marketing
PostgreSQL
Database and pgvector
Microsoft SQL
Database
Snowflake
Data warehouse
BigQuery
Google data warehouse
Databricks
Data platform
Redshift
AWS data warehouse
Synapse
Azure data warehouse
Supabase
Managed Postgres
Workday
Payroll and HR
QuickBooks
Accounting
Sage
Accounting and ERP
Xero
Cloud accounting
Shopify
Catalogue and orders
WooCommerce
Catalogue and orders
Magento
Catalogue and orders
Stripe
Payments and subscriptions
Google Drive
Documents and folders
CSV y Excel
Flat files
Nothing in this category
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.
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 →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.
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.
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.
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.
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.
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.
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.
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.
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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