Custom AI software
We design and build LLM-based products: web services, internal tools, knowledge-base assistants, document generation.
- LLM
- RAG
- Fine-tuning
- Web / Mobile
Build · Integrate · Support
We build software powered by AI
We build AI products around your business, embed models into your processes and take them to a measurable result — from audit to production support.
- for a process audit
- 3–5 days
- for a process audit
- to a working pilot
- 2–4 weeks
- to a working pilot
- of the code is yours
- 100%
- of the code is yours
Scroll — the factory powers on ↓
Stack
OpenAI
Anthropic
Google Gemini
Mistral
Llama
PostgreSQL · pgvector
Salesforce
HubSpot
Slack
Telegram
n8n
MCP
01 · Services
What we do
Four directions, one approach: your process numbers first, code second.
AI integration
We find the processes where AI pays off fastest and embed models into your CRM, ERP, email and messengers — no tool switching.
- Audit
- ROI
- CRM
- ERP
AI agents & automation
Agents triage requests, draft documents and move deals forward. A human approves only the decisions that matter.
- Agents
- MCP
- n8n
- Function calling
Voice
Voice assistants
They answer calls, qualify leads and book clients 24/7 — with a transcript and analytics for every conversation.
- Realtime API
- Telephony
- Call analytics
Voice
An assistant that hears your customer
Connected to telephony and CRM: the agent answers, clarifies details and hands complex cases to an operator.
- →Inbound and outbound calls
- →Bookings into calendar and CRM
- →Transcript and review of every call
02 · How it works
The line where your product gets built
Every project passes five stations. At each one you see status, artifacts and metrics.
Hover — the factory powers on
- 01AuditProcesses, data, impact sizing3–5 days
- 02PrototypeA working version on your data2–4 wks
- 03BuildModels, agents, integrations, testsiterations
- 04RolloutSystems integration, team training1–3 wks
- 05SupportMonitoring, improvements, SLA24/7
03 · How we build
Transparent down to the line of code
You see working code, tests and model quality metrics at every step — not slide decks.
sales-agent.ts
1import { agent, tool } from '@vorcl/agents'
2
3export const salesAgent = agent({
4 model: 'claude-opus',
5 instructions: 'Qualify the lead and book a meeting',
6 tools: [
7 tool.crm('hubspot', { scope: ['leads:write'] }),
8 tool.calendar('google'),
9 tool.knowledge('pricing.pdf'),
10 ],
11 approval: { when: 'discount > 10%', to: '@head-of-sales' },
12 eval: { dataset: 'calls.jsonl', minScore: 0.9 },
13})
14
15// ✓ tests passed · eval above threshold
Every release verified
Unit, e2e and model evals in CI.
Tests
✓ unit · e2e · eval
release blocked on failure
Impact in numbers
Time, money and answer quality — before and after.
Metrics
response time · accuracy
cost per request
Your data stays controlled
On-premise models, action log, role-based access.
Security
PII masking
audit log · RBAC
04 · Agents
The agent works, you decide
Agents propose changes — a human accepts or rejects them. This is how we review agent work.
+0 −0
- awaiting review
05 · Integrations
AI inside the tools you already use
We connect to ERP, CRM, communication channels and data — via API or MCP.
- 01AI modelsWe pick the model per task and can switch providers without a rewrite.OpenAI · Anthropic · Gemini · Meta Llama · Mistral · DeepSeek · Grok · Qwen · YandexGPT
- 02Data & infrastructureVector search, open-source models on your servers, agent frameworks.PostgreSQL · MongoDB · Redis · Supabase · Qdrant · Hugging Face · Ollama · LangChain
- 03Business toolsCRM, channels, docs, payments and automations your team already runs.HubSpot · Telegram · WhatsApp · Notion · Gmail · Google Drive · Stripe · n8n · Zapier · GitHub · Jira · Airtable
Not on the map? Any system with a REST, GraphQL or MCP interface connects too — including 1C, amoCRM and Bitrix24.
06 · What you get
Results you can verify
01
Impact sized before kickoff
The audit shows where AI pays off and what it is worth in hours and money.
02
Metrics at every step
Response time, accuracy, cost per request — before and after rollout.
03
Code and data are yours
We hand over the repository, docs and access. No lock-in.
07 · Engagement
Where to start
We fix the price after the audit — every stage is itemised.
Start
AI audit
Fixed · 3–5 days
We map your processes and show where AI pays off.
- →Process map
- →Impact estimate per opportunity
- →Rollout plan
Popular
Pilot
Estimated · 2–4 weeks
A working prototype on your data and one process.
- →Prototype in a real environment
- →Evals on real cases
- →Impact report
Scale
Product development
Estimated · from 2 months
A dedicated team builds and evolves your AI software.
- →Team: PM, ML, full-stack
- →SLA and 24/7 support
- →Code and knowledge transfer
08 · FAQ
The essentials
How much does an AI product cost?+
It depends on the number of processes and integrations. After the audit you get a fixed pilot estimate and a staged plan.
Where is our data stored?+
Wherever your policy requires: your cloud, your servers or a vetted provider. Personal data is masked before it reaches a model.
Can we run models on-premise?+
Yes. We select open models for the task and deploy them inside your perimeter when data cannot leave it.
Who owns the code?+
You do. We hand over the repository, documentation, infrastructure as code and run a knowledge transfer.
What if the AI makes a mistake?+
Risky actions require human approval, and quality is checked against an eval set before every release.
We reply within one business day
Tell us the task — we’ll show a plan and a prototype
Describe the process you want to speed up. We’ll come back with questions and a proposed format.