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AI agents for business: from pilot to production
We build a production agent for your process: it calls tools through the CRM, ERP, and warehouse APIs, keeps task context in memory, and passes an evaluation on 100–300 real tasks before launch. A person approves risky actions, and monitoring shows the cost per step, the step count, and tool errors.
The agent runs a multi-step process across CRM, ERP, and the knowledge base: it calls APIs, keeps context, and hands risky steps to a person.
Pricing
from ₽1,200,000
per project
This is the lower bound of a project. We scope the exact quote after a brief: it depends on volume and the number of integrations.
Capabilities
Action through an API
The agent creates an order, files a task, and updates a card: every step goes through your system's API under a technical account's rights.
Evaluation before launch
Before production the agent runs 100–300 real tasks from your history: we measure step accuracy, cost, and the share of handoffs.
Rights and approval
Risky actions such as a refund or a status change wait for an employee's approval, and role rights limit access to data.
Production monitoring
Langfuse shows the cost per task, step count, latency, and tool errors: the team uses this data to fix prompts and scenarios.
From request to result
1
Task discovery
We walk the process step by step: which tasks the agent takes, which systems it needs, and where an employee approves an action.
2
System design
We build the tool and permission map, prepare a set of 100–300 tasks with reference answers, and design the memory schema.
3
Pilot agent
We write the agent on LangGraph, connect the APIs and the knowledge base, run the task set, and fix scenarios from the errors.
4
Evaluation and launch
We compare answers with the reference, measure accuracy and cost per task, then move the agent to production with monitoring.
Check if it fits you
Good fit
A process with APIs
Your CRM, ERP, or warehouse exposes data through an API: the agent reads statuses, creates records, and returns the result to the system.
An owner and real tasks
The process has an owner and a history of 100–300 tasks: we measure accuracy on that history before launch.
Not a fit
A process without APIs
Data lives in files and manual exports: the work starts with an integration or migration, then the agent.
Full autonomy over money
Decisions on payments, refunds, and legal obligations stay with an employee; the agent prepares the data and a proposed action.
What you get
6 wks
From pilot to production
Six weeks cover the process review, the pilot agent, and an evaluation on your tasks; production starts with monitoring and a fix plan.
100–300
Tasks in the evaluation set
That many real tasks with reference answers the agent runs before launch. The set defines the accuracy bar and the base for repeat runs.
33%
Gartner forecast for 2028
Gartner expects agentic functions in 33% of enterprise applications by 2028 (Gartner, 2024).
What the delivery includes
Task and tool map
A list of tasks the agent takes, the available APIs, and the rights for each system. The map shows the pilot boundary and the scope of work.
Pilot agent
A working agent on LangGraph with tools, memory, and approval for risky steps. The code, configuration, and runbook go to your team.
Task set and report
A set of 100–300 tasks with reference answers and a report on accuracy, cost per task, and tool errors. The report is the fix plan after launch.
How the system is built
Planner and tools
The planner on LangGraph splits the task into steps and picks a tool: read an order, create a record, search the knowledge base. Every call returns a result to the graph, and the agent picks the next step from the fact.
Memory and context
Task context lives in PostgreSQL, and the agent finds regulation fragments and conversation history through pgvector. Memory holds the thread of the conversation and passes facts between steps, so the employee sees the full picture.
Evaluation and approval
A set of 100–300 tasks with reference answers shows step accuracy and launch cost. The agent sends risky actions to an employee for approval, and the step reaches the system after the reply.
Where the solution pushes back
- 01
You need a process owner
The owner sets the step rules and takes disputed cases. Their involvement speeds up the pilot and keeps the scenarios current.
- 02
Integrations drive the timeline
Every system adds access and field-mapping work. Three integrations stretch the launch, so we fix the system map at the start.
- 03
Evaluation needs a task set
Accuracy is measured on real tasks with reference answers. The owner provides 100–300 examples, and this set defines the quality bar.
Technologies
The technologies we use in our work and their role in a project.
The agent stack
The planner on LangGraph calls models through OpenAI API, task state lives in PostgreSQL, knowledge search runs through pgvector, and Langfuse collects traces and scores.
Cost calculator
This is an indicative range, not a quote. We scope the exact estimate after a brief: it depends on volume and the number of integrations.
Agents
System integrations
Knowledge base size
Add-ons
Support
Year one: updates and monitoring, a reply within 2 business days.
Service
AI agents for business: from pilot to production
The estimate covers our team's work. Hardware, third-party service licences and travel are added on request.
Frequently asked questions
Where does the agent write results?
Into the systems where work happens: a CRM card, an ERP order, a tracker task. The agent writes every action through an API and leaves a record in the step log.
How do you measure accuracy?
On a set of 100–300 real tasks with reference answers. We measure the share of correct steps, the share of tasks without edits, and the cost per task, and we hand over the report before launch.
How long does the project take?
The pilot takes six weeks: process review, tool map, agent, and evaluation. A full launch with several systems and approvals takes 8–12 weeks.
What if a system has no API?
We build the integration first: an export, an event bus, or a wrapper over the database. The agent connects once data moves through a stable channel.
What teams add to this service
Related Services
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Implementation Examples
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Request handling time
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