ChatGPT and Claude are powerful out of the box. But the real leverage comes when they're integrated directly into your tools, your data, and your workflows. That's what we build.
Most business owners use AI by copy-pasting things into ChatGPT. That's fine — but it's not transformation. Real AI leverage comes from integration: when your CRM automatically summarizes customer calls, when your operations dashboard uses AI to flag anomalies, when your team has a custom assistant trained on your company's knowledge base.
We design and build those integrations. We connect AI models to your existing systems — your data, your tools, your workflows — so the intelligence lives inside your business, not in a separate tab someone has to remember to open.
No custom model training required in most cases. We leverage what already exists — OpenAI, Anthropic, and open-source models — and point them at your context. Fast to implement, high ROI, no PhD required.
A structured build process that goes from use-case definition to live integration without disrupting your operations.
We start by identifying the highest-value AI integration opportunities in your business. Not every process benefits from LLM integration — we focus on the ones where natural language understanding creates real leverage: document analysis, customer communication, reporting, knowledge retrieval, and decision support.
We map what data you have, where it lives, and how it flows. AI integrations are only as good as the data feeding them. We assess data quality, identify gaps, and design the architecture so the AI has the right context to do its job well.
We build the integration — API connections, prompt engineering, output formatting, error handling, and edge-case testing. We don't ship until it works reliably in real conditions, not just demos.
We train your team on how to use and maintain the integration, establish feedback loops, and iterate on prompt quality and output format over the first 30 days. AI integrations get better with use — we build that improvement process in from day one.
A regional CPA firm was losing 6–8 hours per new client engagement to manual document review, data entry, and compliance checklist completion. Teklani implemented an LLM-assisted intake system that auto-extracts key data from uploaded client documents, pre-populates engagement files in their practice management software, and flags compliance gaps before a partner ever touches the file. The model was trained on the firm's specific document types and compliance requirements — not a generic tool, but one that understands their exact workflow.
Let's spend 30 minutes mapping it out. We'll identify your top integration opportunity and what it would take to build it.