A chat wired into the system, agents, automations and model evaluation — with numbers, not promises.
A company that wants AI to do the work, not to answer FAQs.
A chat that calls tools and actually does something. A worker that fetches and processes content on a cycle. Automations tying together the tools you already use. Plus model evaluation, so the choice does not rest on whatever was fashionable last month.
File processing lives here too: a phone recording, a scan, a document. This is where other people’s implementations break most often — the model gets a file it cannot read and writes an answer "about" it instead of saying it could not manage.
A chat that does not only answer but does things: reads the content, creates entries, attaches files, hands over to a human.
Our chat has 23 tools it drives itself, and it can take several steps inside one answer. Attachments are handled per type: an image is seen as an image, a recording goes through ffmpeg into a segmented transcription, a document through text extraction with a character limit.
A file that cannot be read produces an explicit error in the prompt and a ban on guessing its content — the model has to say so, not write an answer "about" the file. That sounds like a detail until you see a note written from a recording the model never heard.
Model choice rests on measurement: 288 attempts, zero errors, 4.2 s per turn, 0.0061 USD per turn — twice as cheap and twice as fast as the previous pick. For content generation, fidelity to the source and the count of critical facts come on top.
What you getThe conversation layer, tool calling, content processing.
Tool boundaries, security (session, model allow-list, prompt length limits), evaluation and model choice.
Private project: Shipped in a private project — an internal application for one of our clients, behind a login. We show the mechanics, the numbers and screenshots on a call.
A separate process that does the job on a cycle: fetch the source, process it, save the result, log it, retry on failure.
The worker we built pulls content from ten platforms, with subtitles where they exist and model transcription where they do not, plus classification, entry generation, deduplication, a failure policy and retries. On top of that, a gateway driving a headless coding agent as its own container, with an async task API.
The things a demo never shows, and which decide whether this survives a month: logs in two separate layers, and states instead of repeated lines. Without that, a loop every 30 seconds wrote the same message about 5,000 times in two days, and progress written as a plain log produced 41,000 rows in the database, of which the interface displayed three.
What you getContent processing, classification, generating the result.
Queue architecture, error policy, log discipline, and keeping the process from eating the database with its own progress.
Private project: Shipped in a private project — an internal application for one of our clients, behind a login. We show the mechanics and the numbers on a call.
Connecting the tools you already use: mail, sheets, CRM, messengers, APIs, schedules.
The engine runs on our server — no subscription per flow and no sending your data to an outside platform. Integration tokens live only as encrypted data inside the engine: not in the repository, not in a `.env` file, not in the code.
Models are not hard-coded. Tiers sit in a table and flows read them through a shared sub-flow, so changing one row swaps the model across every automation at once.
What you getFlow design and the nodes that process content.
Token security, project isolation, error handling and provider rate limits.
A capability, not a case study: Honestly: the instance runs and the model tier layer is proven, but the register of production flows is still empty. What we sell here is a ready capability, not a portfolio of automations.
Images for the content, the OG image, editing and upscaling, voice-over, transcription, OCR — no stock photos, no outside designer.
Everything goes through one tool on the server. The token never reaches a client repository or CI secrets. The code holds no model name, only a tier — there are eleven. Swapping a model in one place works across the whole fleet, which matters when new models ship every few weeks.
Files are always pulled down into the repository or the client’s storage, because the provider’s URLs expire after about an hour. We do not generate images that pose as documentation — team photos, interiors, products, events — or the likeness of real people.
What you getThe whole generation and processing layer.
Choosing the tier, reviewing the result, alt texts, and making sure an image never poses as documentation.
A site audit, a code audit after "vibe coding", model evaluation, or market analysis before you build.
Site audit: performance measured as a median, technical SEO, structured data, accessibility, form and header security. The result is a report ordered by payoff, plus what to reject and why.
The code audit after "vibe coding" is the most current service of 2026. We look for exactly the things that happened to us: caches with no boundary, data that passes validation and still makes no sense, no limits towards external APIs, secrets in the repository, no healthcheck, logs writing the same line thousands of times.
Market analysis before building: does the idea hold, who already does it, what does it cost, what can go wrong. We have two such documents and both of them stopped a build until the assumptions were validated.
What you getReviewing the code and the content, a first pass of the report.
Recognising the errors of meaning and operation that syntax never shows, and setting priorities honestly.
The chat and the agents are deployed in a private project, so there is no public address. On a call we show the mechanics, screenshots and the model evaluation results.
The one that won the measurement for that task. The code holds no model name, only a tier — so swapping it is one change, not a rewrite.
Keys stay server-side, the model is picked from an allow-list, and the length of content injected into a prompt is capped with an explicit marker when it is truncated.
We estimate in hours, after a phone call and a look at the project. Our latest projects took 15 to 45 hours.
