Communications
Emails, calls, messages and meetings require analysis, memory and a next step.
The AI agent understands context, drafts a reply, captures agreements and suggests an action.
Artificial intelligence for business
Artificial intelligence for tasks ordinary automation cannot handle.
We build AI agents for your business: they understand context, work with data, prepare decisions and trigger actions by rules.
We start with one AI scenario: define where artificial intelligence can create measurable value, connect your data and build the first working contour in 2–3 weeks. A human stays in control of critical actions.
2–3weeks for AI contour
250K+starting budget
13+years of engineering experience
Senior teamData · APIs · your environmentMetrics from logsPrivate-firstNDA by default
Where an agent pays back faster
First we find the place where money already leaks.
We don't sell a box or ask you to believe in magic. First we choose one scenario where AI can work on your data and produce a result visible in logs, documents or reports.
Documents
Contracts, regulations, requirements and spreadsheets are read manually and differently by each person.
The agent extracts meaning, flags contradictions, prepares a conclusion and links to the source.
Knowledge and support
Answers live in heads, chats and old files. New employees ask people again.
The agent answers from the knowledge base, respects access rights and shows document gaps.
Payback self-check
If the answer can be counted, the agent can be tested.
We don't ask you to believe someone else's percentages. We take your current number, launch the first working contour and compare before/after on one agreed metric.
Communications
How much time goes into parsing emails, calls, meetings and promises?
Metric: reply preparation speed, share of captured agreements, next-step quality.
Documents
Which documents do people read manually again and again even though the conclusion can be assembled automatically?
Metric: document analysis time, completeness of extracted terms, number of detected risks.
Support
What share of tickets is routine but still takes operator time?
Metric: share of routine answers with source, classification speed, escalation quality.
Backoffice
Where do statuses, owners, acts, approvals and promises get lost most often?
Metric: every request has status, owner, deadline and next step.
If at least one question hurts, it is a good candidate for a first contour. On the review we'll say what can be tested in 2–3 weeks and what is better left untouched.
Check your process →When it makes sense to start
If the process repeats,part of it can move to an agent.
The best first tasks are not abstract “digital transformation”, but processes where people read, compare and make routine decisions every day: customer requests, internal requests, contracts, regulations, reports, knowledge bases.
They already have context, data, rules and a cost of error. So you can quickly agree on a metric: response speed, review coverage, extraction accuracy, recommendation quality, share of actions without manual routine.
If the metric cannot be named, you need diagnosis first, not development.
Why you can trust us
We don't sell access to a neural net. We build a system that works inside your process.
Our team has practised software engineering since 2012 and completed more than 50 projects across industries. The portfolio ranges from document workflows for government organisations and environmental monitoring with hardware to Oculus VR simulators and SaaS platforms for startups. These are two worlds: government projects and startups, where our engineers have spent more than 13 years.
DevNeuroX is a studio where AI tools are embedded into a working engineering team, not replacing it. That's why we build not «weekend prototypes», but production systems: faster, without bloated budgets, under senior-engineer control. Source code, documentation and architecture decisions stay yours.
For the client, this means we can put a complex system into operation. When an agent reads documents, respects access rights, works with internal data and keeps an action log, the job requires engineering discipline rather than prompt tuning.
2012
Year: team's practice started
13+
Years of team's engineering experience
50+
Completed projects
01What we do
Two clear ways in: for your task.
If the business needs a new capability, we build an AI agent. If you need a product, MVP or internal system, we move into custom development. In both cases artificial intelligence must work in a real environment, not stay a polished demo.
01 · Custom development
Product or internal system development
MVP, SaaS, account portal, mobile app, backend, 1C, ERP and internal API integrations or legacy rewrite. Faster than classical development, but with proper architecture, source code and documentation.
MVPSaaSMobileBackendIntegrationsLegacy → new
from 1.2M RUB·MVP in 4–6 weeks
Details →02 · Ready-made directions
AI agents for a concrete process
We take one scenario: communications, support, documents, knowledge, backoffice, research or delivery control. We connect data, set rules, measure one metric and show whether it works.
CommsSupportPersonalRAGResearchEngineering Governance
from 250K RUB·2–3 weeks
Details →The most common ad entry is an agent for one process: it shows value faster and limits budget risk. Custom development comes in when a new product or deeper system change is needed.
02Cases
What we've built.
Clients are usually under NDA, so we show the industry, the task, and the measurable impact. Two public projects (Digital Padel, ecological monitoring): to show the range: from a product VR startup to an industrial regulated system.
45+ more projects under NDA
Want cases from your industry: we'll send relevant ones.
Over 13+ years of the team's engineering practice: more than 50 projects across industries: fintech, retail, regional projects, e-commerce, industrial, sports. Most are under NDA, so we only publish what's been cleared. Tell us your industry and task: we'll send relevant cases and references from past clients.
03How we work
Six principles: the same for development and for agents.
Six principles we work by: protecting what matters to you: your money, your data, and your control over the result.
01
You pay for the result, not the process
We fix a measurable metric before the start and measure it on your data. If we don't hit the agreed result: we keep working at our cost. You risk a couple of weeks, not the budget.
02
We ship to production, not to a slide deck
Only a small share of AI initiatives reach production: the rest live on slides. We stay on the job until the agent actually works in your process and delivers, not until it's «successfully demoed».
03
We count in your money
First we find where profit leaks and estimate the effect in money. The price is tied to what you'll recover, not to «a range from thin air». A conversation about payback, not a price list.
04
Your data never leaves your perimeter
We deploy inside your perimeter in line with data-residency law, connect to 1C, ERP, email, documents, internal APIs and data stores. Every agent action is logged, critical steps go through human approval. No vendor black box.
05
Knowledge and code stay with you
The process, the knowledge base and the source code are your property. Open stack, no vendor lock-in: evolve it in-house or switch contractors without rewriting from scratch. Knowledge doesn't walk out with people.
06
Senior engineers, not «a new AI startup»
13+ years of the team's engineering practice, government and startups. Architecture, security and load: handled by people, AI speeds up the routine. You get production reliability the business runs on, not a weekend prototype.
04Why clients feel safe with us
We take responsibility for a working result.
Clients don't care about models and prompts as much as they care that the solution works, doesn't break processes and has a clear owner. So we build around result, control and support.
01
We start with money, not technology
On the first call we review the process, flow volume, cost of error and metric. If the agent cannot pay back on a clear number, we don't sell development.
02
We integrate into your stack
1C, ERP, email, documents, chats, EDI, internal APIs and knowledge bases. The agent must work where the process already lives, not force the team to move to a new system.
03
We keep humans in control
Critical actions go through approval. All decisions and actions are written to a log. This reduces risk for IT, legal and the process owner.
04
We bring it into operation
Our team has practised software engineering since 2012. We build operational systems with access rights, logs, monitoring, documentation and support.
05Before the call
Formulate the task into a clear brief.
If it is hard to describe the task for a vendor right away, start with a short dialogue. The agent helps assemble the initial brief: what needs to be automated or built, what data exists, which systems are connected, and what metric matters to the business.
What you get
- 01Briefly describe the process, product or problem where money and time are lost.
- 02The agent will ask follow-up questions about data, roles, integrations and constraints.
- 03In the end it assembles a structured brief you can pass to us with the request.
- 04With the brief we can faster say what is realistic for the first contour, timeline and budget.
Important
This does not replace a conversation with the team and is not an automatic project estimate. It is a fast way to arrive at the first review with a clear task formulation.
~/neurox: brd-agent
Agent is typing…
06Training
AI should become a working tool for the team. We help make it part of daily work.
In 2–3 years, AI tools will be standard across engineering and business teams. It is better to learn them before the transition becomes urgent. Training helps teams make tools and agents part of real workflows instead of dropping them after the first experiments.
01
Corporate programs for adopting AI in development
This 2–4 week program is built for engineering teams. We review the stack, select AI tools for current tasks and teach the team to use them in active projects. The goal is regular production use across the team.
CTO, tech leads, developers · teams 5–50+
02
Client team onboarding for working with AI agents
After deploying a Sales, Personal, Support, RAG or another agent, we show the team how to work with it. We cover scenario adjustments, prompts for routine tasks, difficult cases and feedback. This keeps the agent useful after the first few months.
Client operational teams · typically after agent deployment
03
«Where to start with AI» mentor sessions for executives
In a 90-minute individual session, we review the CEO's, CTO's or owner's task. We select suitable AI tools, connect them to business goals and produce a concrete roadmap.
CEO, CTO, owners · 90-minute sessions
04
Open learning materials in the Telegram channel
The @dxaiblog Telegram channel offers free reviews of AI tools, lessons from real projects, common mistakes and updates on new models. Engineers, owners and product managers can subscribe. This is the public part of our training, with no commitment.
Anyone exploring AI · public channel · free
Cost depends on team size, format, program depth and the current process. We also account for whether the practice must be built from scratch or improved in place.
You can start for free in the Telegram channel @dxaiblog. We publish open materials and practical breakdowns there.
Next step
We'll review the process, metric and first working contour
We reply during business hours. On the first review we'll say where an agent can pay off, which metric proves it, and where budget is better not spent.