Automation, data and AI engineering for operations that can’t afford mistakes.
From AI-generated leads to the report that lands on WhatsApp at 7:15 am: I design, build and run systems that take manual work off sales and operations, with measured numbers to back it up.
faster ETL: from court PDFs to a spreadsheet in the CRM
8×
n8n
Power Automate
TypeScript
Python
FastAPI
React
Next.js
PostgreSQL
SQL Server
Redis
Docker
Gemini
LightGBM
Claude
OpenAI
Bitrix24
SharePoint
Power BI
Playwright
n8n
Power Automate
TypeScript
Python
FastAPI
React
Next.js
PostgreSQL
SQL Server
Redis
Docker
Gemini
LightGBM
Claude
OpenAI
Bitrix24
SharePoint
Power BI
Playwright
Automations
The SDR opens the CRM and the lead is already scored.
This is the real path of a flow in production, with generic labels. Switch to "Failure handling" and watch the backup model and the JSON fixer take over.
WebhookCRM · new lead
EnrichmentPublic registry + bureau
LLMScore + rationale
ValidationJSON schema
CRMWrites score
Score tableUpsert per lead
WebhookCRM · new lead
EnrichmentPublic registry + bureau
LLMScore + rationale
Backup modelIf the primary fails
JSON fixerRepairs invalid output
ValidationJSON schema
CRMWrites score
Score tableUpsert per lead
A CRM webhook receives the lead, it is enriched with public and credit-bureau data, an LLM returns a schema-validated JSON score, the score is written to the card and to a score table.If the primary model fails or hits its limit, a backup model takes over; if the answer comes back as invalid JSON, an automatic fixer repairs it before validation. A cache avoids paying twice for the same lead.
New field, new pipeline, new automation. No code, no migration, no waiting on IT.
170public API operations
Metadata-driven platform: custom pipelines, visual automations, a documented public API and an MCP server for AI agents. Monorepo with web, worker and decisions recorded as ADRs.
Describe the market and get leads with verified records, ready for sales, straight into the CRM.
27.4kdeals trained the discard model
Outbound prospecting platform combining generative AI and cascading registry checks: the user describes a market, AI suggests the sectors, every company goes through the same registry rule and the result lands in the CRM in batches.
Cascading registry lookup, with the same rule at every layer.
Batch import with live progress, exponential retry and per-job rollback.
Two-phase, idempotent rescue of discarded deals.
A calibrated LightGBM model that learns why sales discards leads.
Decisions and tasks from every meeting, with recordings and history on your own server.
3platforms: Meet, Teams and Zoom
A bot that joins Meet, Teams and Zoom, records, transcribes with speaker labels and extracts decisions and tasks. Swappable transcription and LLM providers, and full-text search across the whole history.
One rule, from sales rep to the board: the number on the card is the same one the API returns.
13dashboards in production
CRM, SQL Server and PostgreSQL under a single business-rule layer. SSO, per-tab and per-action permissions, daily snapshots and panels for executives and office TVs.
The AI reads, classifies and names the customer package, files it in SharePoint and flags what is missing.
94%accuracy on document type
Unzips the customer upload, identifies each document type with multimodal AI (Gemini and Claude), standardizes the name and stores it in the right SharePoint folder, with no manual triage.
88% less time reading creditor lists, with tax IDs validated and liabilities matching the printed total.
0%liability deviation (was 46%)
Combines native text, OCR and LLMs to extract creditors from PDFs, scans and spreadsheets, validates tax-ID check digits and reconciles liabilities before attaching to the CRM.