Generative AI development services for mid-market teams

We build custom LLM applications designed around your data and workflows, so the software fits your business instead of forcing your team to fit a template. This is where custom generative AI development services start for most teams: a process that eats skilled hours, and off-the-shelf tools that never quite fit. We build AI assistants, content generation tools, and intelligent search on GPT, Claude, Gemini, and Llama, in Python and TypeScript, wired into your existing stack. It is a form of generative AI software development tuned to your systems, and it draws on the same team as our broader AI software development work.

We build RAG systems that ground the model in your documents and knowledge base, so answers come from your real information instead of a confident guess. Retrieval-augmented generation (RAG) is the single biggest lever on accuracy, and it cuts hallucinations sharply. When your team keeps re-asking questions whose answers are buried across wikis, PDFs, and tickets, a grounded system hands them answers they can act on. We choose the right vector database, structure your content so the model can find what it needs, and measure retrieval quality over time with LangChain and LlamaIndex. We built Prosci an AI knowledge assistant on OpenAI this way, and it now serves over 10,000 users across 50+ countries.

We turn piles of unstructured documents into structured, usable output: extraction, summarization, and review-ready drafts from reports, contracts, forms, and claims. Work that took your team hours of manual review runs in minutes, with a person checking the result instead of producing it. When the backlog only grows, and manual review is slow and inconsistent, this is where generative AI pays back fastest. We build pipelines that apply your business rules and produce drafts your team can trust. For a healthcare insurance platform, our system cut claim-review turnaround from 3 to 4 days to under 24 hours at 85 to 90 percent accuracy.

We build conversational AI that does more than answer: it understands context, reaches into your systems, and takes action. Ask it, and it can update a record, pull live data, or trigger a workflow, not just return a canned reply. When your support queue is full of questions your documentation already answers, an assistant that acts frees your team for the cases that actually need a person. We wire these assistants into your internal systems with the guardrails to run in production. 

We build copilots that work alongside your team inside the tools they already use, speeding up a specific job instead of replacing it: drafting, research, coding support, or a decision aid. When your best people lose a third of their week to necessary but repetitive work, a copilot hands that time back for the judgment calls only they can make. We build each one to fit your existing workflow and connect to your data, closely related to our AI agent development work, when the tool needs to act on its own.

We embed generative AI into the platforms you already run, using APIs to move work between tools without the copy-paste and app-switching that slow your team down. When work stalls in the handoffs, with data re-entered between your CRM and internal apps and tasks waiting on someone to move them, the AI becomes part of the system instead of another tab. We connect LLMs to your workflows through Salesforce, HubSpot, and internal APIs, part of our broader AI automation services.

We help you find where generative AI actually pays off before you commit a budget to a build. A proof of concept is a small working version, built on your real data, that proves the approach in weeks instead of arguing it in slides. When you are under pressure to do something with AI but unsure which idea is worth building, this is the fastest way to a clear answer. We review your workflows, pick the highest-impact use case, and build a focused proof of concept in two to four weeks, the same front door as our AI consulting services. You see real output and validate value before funding a full build.

We keep generative AI reliable after launch, the part most teams underestimate: evaluation harnesses, output monitoring, prompt versioning, cost controls, and the guardrails that catch quality drift before your users do. Your AI works on launch day, then a model updates, a prompt changes, your data grows, and accuracy slips before anyone notices. We build evaluation pipelines during the proof-of-concept phase with tools like Braintrust and Langfuse, then rerun them every time a prompt, model, or use case changes, combining automated checks, LLM-based scoring, and human review where the stakes are high.