Custom AI-Powered Apps
LLM integration for software you already run: AI search, extraction, drafting and summaries added safely to your product, portal or platform.
What you get
You already have a product, a portal or an internal platform that works. Customers or staff use it every day. Now everyone is asking where the AI is, and you want to add it in a way that actually helps rather than bolting on a chat box nobody uses.
Our LLM integration work adds large language model features to existing software. That might be a search bar that understands plain questions over your help docs, a form that fills itself from an uploaded PDF, a "draft reply" button in your support view, or a weekly summary of account activity. We work inside your codebase or alongside it through an API, depending on what your team prefers.
The hard part is rarely calling the model. It's choosing the right feature, getting it accurate on your real data, handling failures gracefully, keeping costs predictable and making sure sensitive information doesn't leak. That's where most of our effort goes.
We work with models from Anthropic (Claude) and OpenAI, and we design integrations so you can change providers or models later without rewriting the feature.
The problem
Most teams can get an LLM feature working in a day. The problems show up later:
Your developers may be great at your product but new to building and testing AI features. We can do the work for them or alongside them, so the knowledge stays in-house.
What we build
Plain-language search across help docs, product data, contracts or past tickets, with answers that cite their sources and respect each user's permissions.
Turn uploaded PDFs, scans and emails into structured fields your product already understands, with confidence scores and a review screen for uncertain values.
Draft replies, descriptions, proposals or notes inside your existing screens, using your tone, templates and the record's context.
Tag incoming requests by type, urgency or sentiment, and route them to the right queue or person inside your existing workflow.
Short written summaries of long threads, account histories or report data, shown where users already look instead of in a separate tool.
A small internal service that handles provider keys, model choice, retries, rate limits, logging and costs, so every feature uses AI the same way.
Scenarios
A SaaS company's support team works in its own admin app. We add a panel that summarizes the customer's account and recent tickets, and drafts a reply that agents edit before sending.
Customers upload purchase orders as PDFs into an existing ordering portal. An extraction step maps line items to SKUs in the catalog and pre-fills the cart, with unmatched lines flagged for the customer to fix.
A retailer's product team writes hundreds of descriptions each season. A drafting feature inside their PIM creates first drafts from supplier specs and brand guidelines, which editors approve.
A professional services firm has years of internal guidance in its intranet. AI search returns direct answers with links to the source documents, limited to what each user is allowed to see.
How we build it
We treat AI features like any other production code, with one extra step: evaluation.
We review your product and your users' biggest time sinks, and rank possible AI features by value, accuracy risk and cost. Often the best first feature is a narrow one, like extraction or classification, rather than a general assistant.
Before writing the feature, we collect real examples (anonymized where needed) and agree what a good output looks like. This gives us a score to beat, and a way to test any future prompt or model change.
We use structured outputs so the model's answer fits your existing data types, retrieval with permission checks when answers depend on your data, and fallbacks when the model fails or times out. Inputs and outputs are logged so you can trace any answer.
We pick the smallest model that meets the accuracy target, trim prompts, cache where safe and track spend per feature. Sensitive fields can be masked before a request leaves your system.
If you're looking for a customer-facing assistant rather than features inside your app, see AI agents and chatbots. If the AI needs data from other systems first, pair this with API integrations and data sync. Our work page shows how we approach real projects.
Yes. We often work in the client's repository alongside their developers, following their conventions and review process. If that's not practical, we build the AI feature as a separate service your app calls through an API.
It depends on the task, the accuracy you need and your budget. We test a few options from Anthropic and OpenAI against your evaluation set and recommend the cheapest one that meets the target. The integration is designed so you can switch later.
Not with the business API tiers we use, which don't train on your inputs by default. We also send only the data a feature needs and can mask sensitive fields. We'll document exactly what goes where for your security review.
We ground answers in your own data with retrieval, ask for structured outputs, add rules for what the model must refuse, and show sources to users. For high-stakes actions we keep a human approval step. No setup removes errors completely, so we measure them.
Running costs depend on request volume, prompt size and model choice. We estimate monthly API costs during the evaluation phase using your real data, and build in usage tracking so there are no surprises after launch.
Tell us about your product, your users and the AI ideas on your list. We'll help you pick the feature most likely to pay off and show you how we'd measure it.
We pick tools for your situation, not ours. These are the ones we reach for most on this kind of work.
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An honest breakdown of what makes a custom AI app cost what it does, how long each phase usually takes, and the running costs that continue after launch.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.