Generative AI features that behave like the rest of your product.
Customers now expect AI inside the software they pay for. We integrate LLMs into existing web and SaaS products with the same discipline as any other feature: tested, observable, reversible and priced so margins survive. Our AI-native engineers get up to speed on your codebase quickly.
Calling an LLM API takes an afternoon. Shipping a feature customers rely on takes engineering.
Most product teams already have a prototype: a button that sends text to a model and shows the result. It impresses in a sprint demo. Then the questions arrive. What happens when the provider is slow or down? How do we know last week's prompt change did not break summaries for our largest customer? Why did the AI bill jump?
LLM integration is the work between that prototype and a feature you can list on a pricing page. It covers the user experience of output that is sometimes wrong, the plumbing that insulates your product from any single provider, and the evaluation that lets you change prompts and models without fear.
We do this work AI-first ourselves. Our engineers use coding agents to read unfamiliar codebases, trace data flows and draft integration code in parallel, then review every change as senior engineers. It is the same technology we are helping you ship, used daily in our own delivery.
Three ways to put an LLM inside your product
The pattern shapes the interface, the failure modes and the cost profile. Most products end up using more than one.
| Inline assist | Background processing | Conversational layer | |
|---|---|---|---|
| What users see | Suggested drafts, rewrites, autocomplete | Tags, summaries and scores in lists | A chat panel over their data |
| When it goes wrong | User edits or ignores it | Silent errors spread through data | Confident wrong answers on screen |
| Latency needs | Streamed, within seconds | Minutes are fine, batch it | Streamed, conversational pace |
| Cost profile | Per use, tracks active users | Predictable, batch discounts possible | Hardest to predict, long contexts |
| Good first feature? | Usually yes | Yes, with spot checks | Only with a narrow scope |
What LLM integration needs beyond the demo
These are the gaps we find most often when reviewing in-house AI features. Each is simple to build early and painful to retrofit.
A provider abstraction layer
Model calls sit behind your own interface, so you can switch providers, route by task or add a fallback model without touching product code.
Structured outputs with validation
When your code consumes model output, enforce a schema and reject or retry invalid responses instead of parsing free text and hoping.
Prompts versioned like code
Prompts live in the repository, change through pull requests and ship behind feature flags, so a regression can be rolled back quickly.
An evaluation suite in CI
Real inputs with expected qualities, scored automatically on every prompt or model change, including model-graded checks that humans calibrate.
Cost tracking per tenant and feature
Token spend by customer and feature, with limits, so one heavy account cannot quietly erase the margin on a plan.
Clear data handling terms
Confirm what your provider retains, where it processes data and whether it may train on it, then reflect that in your own customer contracts.
From one AI feature to a repeatable capability
Pick the feature
Week 1We review your product, usage data and customer requests to choose one feature with clear value and a tolerable failure mode, then define what good output looks like.
Lay the foundations
Weeks 1 to 3Provider layer, tracing, cost tracking, evaluation harness and feature flags, shaped to fit your existing architecture rather than bolted beside it.
Ship to a cohort
Weeks 3 to 6The feature launches to selected customers, with feedback controls and traces that show exactly which inputs produced poor results.
Hand over the pattern
OngoingYour team gets documentation and a worked example, so the second and third AI features reuse the foundations instead of starting again.
Pricing generative AI features without eroding margins
Generative AI adds a variable cost to software that used to have almost none per action. Before launch, decide whether AI is bundled into existing plans, sold as an add-on or metered through credits. Each option changes how customers behave and how much usage you should plan for.
Model choice is a commercial decision as much as a technical one. Smaller models handle classification and short rewrites for a fraction of the price of frontier models, and prices keep falling. The provider layer and evaluation suite are what let you switch to cheaper or better models safely when they appear.
Questions we often hear
Which LLM provider should we integrate with?
The right choice follows the task, your data residency needs and cost. We usually test two or three models against your real inputs before choosing, and build an abstraction so the decision stays reversible. Some products pair a commercial API for quality with a smaller open-weight model for high-volume tasks.
How long does it take to add an AI feature to an existing product?
A well-scoped first feature with proper foundations commonly takes a few weeks with an AI-native team, depending on how accessible your codebase and data are. Later features move faster because they reuse the same provider layer, evaluation suite and monitoring.
Will our customers' data be used to train AI models?
Business API terms from major providers generally exclude training on your data by default, but retention periods, processing regions and exceptions vary, so we check current terms for your chosen provider. Where contracts or regulation rule out third-party processing, a privately deployed model is an option.
How do you test features built on LLMs?
We build an evaluation set from real inputs, score outputs automatically on every change with a mix of rules and model-graded checks, and have people review a sample to keep the automated scores honest. Production traces and user feedback add new cases to the set over time.
Do your engineers use AI tools when working on our codebase?
Yes, with your agreement on tools and data handling. Coding agents help us understand an unfamiliar codebase quickly, run several pieces of integration work in parallel (we built the open-source perchd CLI for exactly this) and generate tests. A senior engineer reviews and owns everything that merges.
Related reading and tools
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