Practical AI automation for real business processes — not demos, not buzzwords. Tools that run in production, integrated into your existing systems.
Repetitive work that takes hours every day — but doesn't have to.
AI-powered support assistant trained on your product documentation. Handles 70%+ of repetitive tickets automatically, escalates the rest.
Automatically extract, classify and route incoming documents — contracts, invoices, applications — using LLMs with validation logic.
Give your team or customers a chatbot that answers questions based on your internal documentation, wiki, or product knowledge base.
AI-drafted responses to emails, forms, or messages — reviewed by humans before sending, or fully automated for structured requests.
Automated data extraction, transformation and summarization from reports, spreadsheets or databases — delivered on schedule.
Multi-step business workflows with AI decision points — from lead qualification to order fulfillment, running 24/7 without human input.
Map the current manual process: inputs, outputs, decision points, exceptions. Identify what's automatable and what isn't.
Choose the right model (GPT-4, Claude, local LLM), design the prompt architecture, retrieval strategy, and fallback logic.
Evals, test suites, confidence thresholds, human-in-the-loop for edge cases. AI systems need validation infrastructure.
API key rotation, data access controls, audit logging. AI tools handle sensitive data — security is non-negotiable.
OpenAI GPT-4/4o, Anthropic Claude, Mistral, or local open-source models (Llama, Qwen) for data-sensitive use cases.
Pinecone, pgvector, Qdrant — for RAG pipelines with semantic search over your knowledge base.
LangChain, LlamaIndex, or custom Python pipelines — depending on complexity and maintainability needs.
For regulated industries — run everything locally with Ollama + open-source models. No data leaves your infrastructure.
We build tools that work in production. That means being honest about limitations.
AI business automation is not about trends and demos — it is about concrete ROI. Companies that have implemented AI tools in routine processes reduce document processing time by 60–80%, lower support load by 40–70% and free their team for work that genuinely requires human judgement.
The biggest AI automation gains are seen in: law firms (automated contract processing and classification), e-commerce (support chatbot handling 70%+ of repetitive questions), logistics (document workflow and tracking automation), SaaS products (AI-powered onboarding and personalised prompts).
Our approach: we do not sell AI solutions — we automate specific processes. We start by analysing your workflow, identify where AI will deliver the best ROI, and build the minimum solution that solves the problem. No unnecessary abstractions or vendor lock-in.
Tech stack: OpenAI GPT-4/4o or Anthropic Claude for generation, pgvector or Pinecone for RAG, LangChain or custom Python pipelines for orchestration, Celery + Redis for async processing. For sensitive data — on-premise solutions with open-source models (Llama, Mistral) with no data leaving your infrastructure.
Integrated with your docs. Handles 70%+ of repetitive questions.
Q&A over your knowledge base, wiki or product documentation.
Automated document processing, classification and routing.
A simple chatbot on OpenAI API starts at $800. A knowledge base RAG system from $2,000. A full document processing AI pipeline from $4,000. Cost depends on logic complexity and integrations.
RAG (Retrieval-Augmented Generation) means the AI answers questions based on your documentation rather than general knowledge. Result: accurate answers about your product without hallucinations.
AI automates routine work — repetitive queries, document processing, report generation. Tasks requiring judgment and context stay with humans. The goal is to free up time, not reduce headcount.
For sensitive data we implement on-premise solutions with open-source models (Llama, Mistral). No data leaves your infrastructure. For less sensitive use cases — enterprise OpenAI with a data processing agreement.
Best ROI is seen in: law firms (contract processing), e-commerce (customer support), logistics (document workflow), SaaS (onboarding and support). Key condition: there are repetitive tasks with clear rules.
Tell us which repetitive tasks eat the most time. We'll assess what's worth automating and how to do it right.
Discuss AI Automation