We don't sell AI as a concept. GeekBot Autopilot below is a live agent we built and run ourselves — this is the same kind of system we build for clients.
Run the Automation Scan
Three processes most businesses run by hand, and what changes when an agent runs them instead.
Before A form submission sits in an inbox until someone has time to read it, sort it, and reply.
After An agent reads the submission, checks it against your criteria, drafts a reply, and only pings your team when it's ready to talk.
Before Every ticket gets manually triaged, tagged, and routed before anyone can act on it.
After An agent reads, classifies, and routes tickets on arrival — your team starts on the actual problem, not the sorting.
Before Someone spends a chunk of every week pulling numbers from three systems into one deck.
After An agent pulls, formats, and delivers the report on schedule. The human reviews it instead of building it.
> cat case-studies/geekbot-autopilot.md
Publishing technical content on a consistent schedule is one of the highest-leverage things a consultancy can do for search — and the first thing to slip when client work gets heavy. Done by hand it means logging into a model, prompting it, copying the output, formatting it as HTML, and pasting it into the site. Every article, every time.
An autonomous PHP agent that generates SEO-optimized content using GPT-4 and auto-publishes it to our CMS — no human in the loop for the day-to-day.
A cron job wakes the agent on a fixed schedule. It takes the next item off a plain-text topic queue, sends it to GPT-4 with a prompt that requires clean HTML back — h1, h2, p, ready to render — and appends the finished article to a flat-file JSON store the site reads directly. No database, no CMS admin, no deploy step.
The guardrails, stated plainly: the topic queue is the control surface, so the agent only writes what's been put in front of it, and an editor dashboard allows posts to be edited or removed after the fact. Nothing sits between generation and publication.
The agent described above, in public. PHP, cron-triggered, GPT-4 — not a diagram of a system we could build, but the one that publishes for us.
If we run an unattended agent in production for our own business, that's not a sales pitch — it's the same category of system we'd build for yours.
> ./pricing --tiers
Start with the scan. It's free, it runs right here, and it tells us which of these actually fits before anyone talks numbers.
A few questions about where the manual work is happening. You get a read on your automation opportunity before you commit to anything.
One process, fully automated end-to-end — lead intake, ticket triage, report generation, or similar.
Multiple connected agents across departments, custom integrations, full architecture.
Monitoring, maintenance, and optimization of agents already running in production.
> Reading: generative_ai_and_mlops.md
Artificial Intelligence has graduated from experimental labs to the core of business strategy. The current wave of Generative AI (GenAI) allows businesses to create content, summarize vast amounts of data, and build intelligent conversational agents that understand context, not just keywords. However, the challenge has moved from "creating a model" to "managing the lifecycle." This is where MLOps (Machine Learning Operations) becomes critical—applying DevOps discipline to data science to ensure models are reproducible, explainable, and scalable.
The Latest Technologies: We are leveraging Amazon Bedrock, a serverless service that makes Foundation Models (FMs) from top AI startups and Amazon available via API. This allows you to build GenAI apps without managing massive GPU clusters. For traditional ML, Amazon SageMaker JumpStart provides pre-trained models that can be fine-tuned on your proprietary data securely, ensuring your data never leaves your environment to train a public model.
How We Execute: We build an end-to-end data pipeline. It starts with a Data Lake on Amazon S3, organized by AWS Glue. We then use SageMaker Pipelines to automate the training and retuning of models. Once a model meets accuracy thresholds, it is deployed to a SageMaker Endpoint. We integrate "Human-in-the-Loop" (A2I) workflows where low-confidence predictions are reviewed by humans, constantly feeding data back to make the model smarter over time.
A few questions about where the manual work is happening. No obligation, no call required to get the result.
Run the Automation Scan