LLM-Powered Applications
Custom applications built on large language models: document analysis, content generation, smart search, and decision support, grounded in your own data and workflows.
// Applications & AI
AI and intelligent automation that reaches production
Most corporate AI initiatives stall between the demo and the deployment. We build the second half, and we know it works because we operate our own AI SaaS in production.
The gap between the demo and Tuesday morning
Every executive has now seen an impressive AI demo. Far fewer have seen one running six months later, wired into the CRM, surviving bad inputs, and saving actual hours. The distance between those two states is engineering: evaluation, retrieval that cites its sources, fallbacks for when the model is wrong, and cost controls so the invoice does not become its own incident.
That engineering is what we sell. Not an AI strategy deck, and not a chatbot widget bolted onto your website by an agency that has never carried a pager for one.
We hold ourselves to a plain standard here: if a workflow does not justify the model call, we will tell you to use a rules engine and keep your money. Some of the best automation we ship has no AI in it at all.
What we build
Workflow & Process Automation
Replace repetitive manual work with AI-driven automation: data entry, document processing, routing, and reporting that run themselves and free your team for higher-value work.
AI Chatbots & Assistants
Conversational assistants that answer questions, qualify leads, and book appointments, trained on your business, your documents, and your voice.
Intelligent Lead & Intake Pipelines
Capture, score, and follow up automatically. The lead who hears back in a minute books the call; we build the fast path first.
Predictive Analytics & ML
Forecasting, scoring, and reporting models wired into the BI tools your team already reads.
AI Strategy & Governance
A practical roadmap with real use cases and real ROI, plus the use policy and data classification that let it survive an audit.
How the engagement runs
- 01Use-case selectionTwo weeks scoring candidate workflows on value and feasibility against your actual data. Most companies have two or three worth building and a dozen that only demo well.
- 02Grounding and guardrailsData boundaries designed first: what the model sees, what leaves your tenant, what gets logged. Retrieval built so answers cite your documents.
- 03Build and evaluationThe application, plus an evaluation harness that measures answer quality before and after every change. This is the step agencies skip.
- 04Production and cost controlDeployment into your cloud, monitoring, fallbacks for model failures, and per-workflow cost budgets with alerts.
Proof, from our own rack
We built and operate four SaaS platforms with AI in the delivery path, including an AI-powered comparative market analysis engine and an automated listing content generator. When a model regression breaks output quality at 2 AM, that is our problem in our own products first. Client systems inherit the habits that experience forces: evaluation before deployment, fallbacks by default, and honest cost accounting.
Models and platforms
- Anthropic Claude
- OpenAI
- Azure OpenAI
- AWS Bedrock
- PostgreSQL + pgvector
- Supabase
- Python
- Node.js
Common questions
Where should we start with AI?
With a short list of use cases scored by value and feasibility against your actual data, not with a platform purchase. Most companies have two or three processes where an LLM removes real hours per week, and a longer list where it would be a demo. The readiness assessment exists to separate the two before you spend.
Can it work on our own data and documents?
Yes, and it should. We build retrieval-grounded systems that answer from your documents, records, and systems, with citations back to the source. Generic chatbot answers are what you get when this step is skipped.
Which models and platforms do you build on?
Anthropic Claude and OpenAI models, run through your cloud accounts where possible: Azure, AWS, or Google Cloud. We pick per use case on quality, cost, and data-handling terms, and we build so the model layer can be swapped without a rewrite.
How do you handle data privacy and governance?
Data boundaries are designed first: what the model can see, what leaves your tenant, what gets logged, and who can access it. For regulated environments we pair build work with an AI use policy and data classification so the deployment survives an audit, not just a demo.
How is this different from what an agency sells as AI?
We operate our own AI-powered SaaS platforms in production, so we carry pagers for this kind of software ourselves. The difference shows up in the unglamorous parts: evaluation, fallbacks, cost controls, and what happens when the model is wrong.
Related services
Bring the workflow that eats your team's week.
Thirty minutes. We will tell you whether AI actually helps, what it would cost, and what we would build first.