We build custom AI agents and software that automate your workflows: from sales outreach and document processing to voice support and local AI deployment. Production-ready, not demo-ready.
Services
Six services. Each one designed for a specific business outcome. No jargon, no surprises — just working systems.
Conversational agents, workflow automation, and internal tools built around your business. Not templates. Systems that take real actions.
Move your OpenAI and Anthropic workloads to open-source models on your own hardware. Same API. Same quality. A different bill.
Extract structured data from invoices, contracts, forms, and scans. Handles tables, multi-page PDFs, and messy handwriting.
End-to-end voice pipelines for customer support, meetings, and voice interfaces. STT, LLM reasoning, and TTS in one system.
Image analysis, video understanding, visual inspection, and GUI automation. Native multimodal models that see and reason.
Test your current AI on real edge cases before your users do. Get a report on what's broken and a plan to fix it.
The problem
Not because the model is bad, because there's no harness, no sandbox, and no evals. Teams ship demos that break on edge cases, leak data, or hallucinate at scale.
Your demo works on 10 test cases. But real users ask unexpected things, upload weird files, and chain requests in ways you didn't predict. Without a harness and evals, you only find out when customers complain.
Every new feature means more API calls. Every user means more tokens. The bill compounds monthly and you have no leverage when the provider raises prices or deprecates the model you rely on.
Your prompts, your data, and your model behaviour live on someone else's servers. One policy change, one price hike, one deprecation notice, and your product is at risk. No fallback, no control.
How we work
The Agent Development Life Cycle. Every engagement walks through these five phases. production AI is not a demo, it's a system.
We scope the agent, choose the model, design the tool flow, and define success metrics. If local models can't match your quality bar, we tell you upfront.
We build the agent, the UI, and the integrations. Conversational, voice, document, vision. Whatever the workflow demands. Built around your stack, not a template.
We test against real edge cases in a sandbox before production. Prompt injection, hallucination, latency spikes, bad inputs. We find what breaks before your users do.
We deploy on your hardware or rented infrastructure in your jurisdiction. Open-source models, OpenAI-compatible endpoints, zero code changes on your side.
We monitor, patch, and iterate. New models get benchmarked against your metrics before any swap. You get direct engineer contact and real response times.
Projects
Real systems built for real workflows. Each project covers a different service we provide.
An agent that researches prospects from LinkedIn, Crunchbase, and Clearbit, then drafts hyper-personalised outreach emails referencing real signals: funding rounds, hiring, tech stack.
A document intelligence system that reads invoices, receipts, and purchase orders from PDFs and emails. Extracts line items, tax, vendor details, and pushes structured data to the accounting tool.
A voice AI pipeline that handles customer calls in 12 languages. STT transcribes, the agent reasons, and TTS responds. All running on the client's own servers for data compliance.
Migrated a mid-size finance firm's AI workloads from OpenAI to local models on rented EU servers. Same API, same quality, full GDPR and EU AI Act compliance.
A vision + document AI system that reads legal contracts, highlights key clauses, extracts terms, and flags risky language. Reduces contract review time from hours to minutes.
A voice AI system that joins meetings, transcribes in real-time, extracts action items, assigns owners, and pushes to project management tools. Supports 12 languages.
Industries
Pick the one closest to you. Each vertical has a typical migration pattern and a typical saving range.
KYC parsing, claims triage, customer support, compliance reporting.
Contract OCR, clause extraction, matter research, due diligence.
Intake summarisation, multilingual patient notes, prior auth.
Multilingual support, product copy, returns, inventory queries.
Voice-to-text, content repurposing, Slack knowledge base, lead routing.
Stack
We do not pick favourites. We pick what wins on your workflow, and we switch when a better one ships. Everything below is what we deploy with today.
Gemma 4 Google · E2B, E4B, 12B, 26B-A4B, 31B. All multimodal. 
Qwen3 Alibaba · 0.6B to 32B dense, 30B-A3B / 235B-A22B MoE 
DeepSeek V4 DeepSeek · Pro (1.6T/49B active), Flash (284B/13B active) 
Mistral Small 4 Mistral AI · 119B MoE, 128 experts, multimodal Mistral Medium 3.5 Mistral AI · 128B dense, 256K context 
Nemotron 3 NVIDIA · Nano 30B, Super 120B, Ultra 550B, Nano Omni 
Liquid AI Liquid AI · LFM2.5 8B-A1B, edge-optimized hybrid architecture 
Moshi Kyutai · Real-time speech-to-speech, sub-200ms Qwen3-ASR Alibaba · 52+ languages, high accuracy 
Whisper OpenAI · Industry standard transcription 
XTTS-v2 Coqui · Multilingual voice cloning Voxtral TTS Mistral AI · 4B params, 9 languages, zero-shot voice cloning Qwen3-VL Alibaba · Document QA, chart parsing, video DeepSeek-VL2 DeepSeek · Reasoning-first visual understanding 
PaddleOCR-VL Baidu · Production OCR, tables, formulas 
Docling IBM · Document parsing and layout analysis Mistral OCR 4 Mistral AI · 170 languages, structured doc extraction, self-hosted 
LangGraph LangChain · Graph-based state machines, production standard 
CrewAI Role-based agents, rapid prototyping 
LlamaIndex Workflows RAG-grounded orchestration OpenAI Agents SDK Lightweight tool-use-first agents 
Flue Open agent framework with durable execution 
vLLM High-throughput LLM serving 
SGLang Efficient structured generation Triton Inference Server NVIDIA · Production model serving 
Ollama Local model management 
llama.cpp Edge and CPU inference 
Docker & Kubernetes Container orchestration 
AWS / GCP / Azure Cloud deployment 
Hetzner / OVH EU GPU instances 
Qdrant Vector database 
PyTorch / Transformers Model training and inference Why us
Small team, senior engineers, direct access. No account managers, no junior developers, no handoffs.
The person who scopes your project is the person who builds it, runs the benchmarks, and owns the rollback. No sales engineer reading from a script.
Model weights are Apache 2.0 / MIT. The harness, the adapters, the prompts, the evals. All yours, all portable. No per-seat, no per-call, no lock-in.
We don't ship demos. We ship systems with harnesses, sandboxes, and evals. Because an AI agent that breaks in production is worse than no agent at all.
FAQ
Don't see your question? Email me, I answer everything a CTO asks in the first call.
AI agents and systems — conversational, voice, document, vision, or custom. We handle the full lifecycle: design the agent, build it, test it in a sandbox, deploy it on your infrastructure, and keep it running. Every engagement includes a harness (observability, retries, tool routing) and an eval suite (automated tests from your real traffic).
Three honest things. One: latency on local models can be worse than cloud for tiny queries — fine for batch, less so for real-time chat. Two: throughput is GPU-bound, so a single RTX 4090 tops out around 30–50 tokens/sec for a 27B model. Three: we are a small studio, not a 24/7 NOC. Production incidents get a real engineer the next business day. We will not oversell scale or pretend a local model can serve 10M users.
We measure on your prompts before we commit. On conversational agents, knowledge bases, document reading, and multilingual support, current open weights match or beat GPT-5.5 on about 8 of 10 enterprise tasks. The 2 that genuinely need frontier reasoning (creative writing, hard math, very long context) we leave on the API and migrate the rest. If the local model cannot match on your success criteria, the audit says so and we do not migrate it.
You do, all three. Weights are Apache 2.0 or MIT, public from day one. The moment they are on your hardware you can run them, fine-tune them, freeze them, or ship a derivative. The harness, the LoRA adapters, the prompts, the evals. All yours, all portable. No per-seat, no per-call, no per-region fee.
That is the whole point of local deployment. Your prompts, your documents, and your fine-tuning data never leave the hardware you own. No third-party API, no per-call log shipped to a hyperscaler, no model that can be unilaterally changed. If a regulator asks where the model ran, the answer is one rack in Frankfurt, not 'we used OpenAI last Tuesday'.
It depends on the model and volume. A 27B Qwen 3.6 or Gemma 4 26B runs on a single H100 or dual-A6000 box. Smaller models (Mistral Small 4, Gemma 4 E4B) run on a single RTX 4090 or a rented Hetzner / OVH instance. For private deploy we deploy to your data centre, your colocation rack, or a rented server in the EU jurisdiction you pick. The procurement list is part of the engagement — no vague answers.
Fast. No sales cycle, no procurement theatre, no six-week kickoff. You send the brief today, discovery call by Friday, first working demo in week two. We are an AI-native studio — the tools we use to build are the same tools we ship. Nothing in the loop slows us down.
A harness is the production middleware that makes an AI agent reliable. It handles retries when the model times out, routes tool calls to the right APIs, enforces rate limits, logs every decision for audit, and triggers human-in-the-loop when confidence is low. Without a harness, your agent is a script that works on sunny days. With one, it's a system that degrades gracefully.
Next step
15 minutes. We tell you which of your AI workflows can move to local, which can become agents, and roughly what it takes. No deck, no sales call, no commitment. Fast reply.