What companies bring us
Document & email automation
Invoices, contracts, support inboxes, extracted, classified and routed automatically instead of read by hand. An LLM reads every document the way your best ops person would: it pulls the fields, validates them against your rules and pushes clean data into the tools you already use. The usual result is hours of manual entry collapsing into a short review queue of edge cases.
AI features in your product
Customer-facing chatbots that answer from your product data instead of improvising, retrieval-augmented generation (RAG) over your own content, semantic search that understands meaning, and generation where your users type the same thing every day. Built inside your existing product, not bolted on beside it.
Internal copilots
Ops, sales and support teams answering from your knowledge base in seconds, with sources cited. We connect the LLM to your wikis, tickets and docs with retrieval that shows its work, so your team can trust the answer and click through to the source.
Image & video generation pipelines
On-brand visuals and video generated at scale with custom ComfyUI pipelines, Stable Diffusion, FLUX, Pony and Wan, served as an API or batch job inside your product. We handle the GPU infrastructure, queueing and cost-per-render so your team just calls an endpoint.
Common questions
Which models do you work with?
Anthropic Claude, OpenAI, and open-source models where data or cost demands it. We pick per use case, not per fashion.
Does it work with our existing system?
That's the point. AI integration means into your product and stack, not a separate tool your team has to remember to open.
What does it cost?
One fixed price for the build after a scoping call, plus a clear projection of the ongoing API costs, both in writing before you commit.
How fast can we be live?
Most integrations ship in 2-4 weeks: a working version early, then evaluation and hardening against your real data.
Will AI actually work in our product, or is it hype?
It works when it is scoped to a real job and measured. We evaluate accuracy on your data before launch, so "it seems to work" becomes "it is right 96% of the time, and here is what happens the other 4%."
How do you keep AI running costs under control?
Model selection, caching and batching designed against a monthly budget from day one, no surprise five-figure API bills.
Is our data safe with AI features?
Yes. GDPR-compliant architecture, EU processing options, your data stays yours, and your code never trains anyone's model, documented for your compliance team.
Can you build a customer support chatbot on our own data?
Yes. We build support chatbots grounded in your help articles, product docs and past tickets using retrieval-augmented generation, so answers come from your content with sources linked instead of the model improvising. The bot hands off to a human the moment confidence drops or the customer asks for one, and every conversation is logged so you can see exactly what it said and why.
What is RAG, and do we actually need it?
Retrieval-augmented generation (RAG) means the model looks up relevant passages from your own documents first and writes its answer from what it found, citing the sources. You need it whenever answers must be grounded in your data: support bots, internal knowledge assistants, document Q&A. You can skip it when the task is transformation rather than knowledge, summarising, drafting, classifying, where the input itself contains everything the model needs.
Can you integrate AI into our existing CRM, ERP or internal tools?
That is most of what we do. AI integration usually means wiring LLM steps into systems you already run: leads scored inside your CRM, documents processed straight into your ERP, tickets triaged in your helpdesk. We work at the API level, so nothing gets ripped out or replaced, and your team keeps working in the tools they know.
How do you measure whether the AI is actually accurate?
Before launch we build an evaluation set from your real cases, actual documents, actual questions, actual expected outputs, and score the pipeline against it. That number decides which model we use and whether a change ships. After launch the same checks run continuously in monitoring, so accuracy drift shows up on a dashboard instead of in customer complaints.
Can you build image or video generation pipelines?
Yes. Beyond LLM features we build full generative-media pipelines on ComfyUI, image models like Stable Diffusion, SDXL, FLUX, Pony, Illustrious and Qwen-Image, and video models like Wan, HunyuanVideo and LTX-Video. We wire them into custom workflows with ControlNet, LoRAs and upscaling, then serve them as an API or batch job on your own GPUs or cloud, so the output is on-brand, repeatable and yours.
How we build.
Unit and feature tests with PHPUnit / Pest. Standard, not an add-on.
Automated tests and deploys on every push. No manual releases.
Every line reviewed by a senior engineer. No juniors on your budget.
Full source code, infrastructure and documentation transfer on handoff.
Bring the use case. We'll tell you if AI is the right tool, and what it costs to ship it if so.