Will adding AI break my existing app?

Not if done right. We treat it as a parallel feature behind a flag, with clean interfaces to your current data and actions. Your core product keeps working exactly as before.

How long does it take to add LLM features to a live product?

A scoped first feature (one workflow, retrieval + 2-4 tools) usually ships in 3–6 weeks after discovery. Deeper integration or multiple surfaces takes longer but we ship value early.

Do I need to change my whole architecture?

Rarely. We work with what you have — REST or GraphQL APIs, your database, existing auth. The new work is the retrieval index, tool layer and evaluation harness.

How do you control costs once users start using the AI?

Aggressive caching of retrieval, smaller models for simple steps, token caps per interaction, rate limiting, and real-time dashboards. We design the cost envelope during discovery.

How to Add LLM Features to an Existing App (2026 Guide)

Key takeaways

The winning pattern is almost always: expose your existing data via retrieval + give the LLM narrowly-scoped tools it can call with user confirmation.
Ship behind a feature flag to a small cohort first, with full tracing and a 'let me get a human' escape hatch.
Most integration cost is in the retrieval layer and tool definitions — not the prompt. Budget for data cleaning and evals.
Adding production-grade LLM features to an existing app typically costs £18,000–£55,000 at Softgen depending on depth of data access and actions.

The short answer

You don't rebuild your app around AI. You add a thin, well-scoped layer: retrieval over your existing data, a small set of tools that map to real actions in your system, guardrails, evals, and a clean UI surface (side panel, inline suggestions, or chat). The app stays in charge; the LLM is a very smart, slightly unreliable intern that can only do what you explicitly allow.

Start with the outcome, not the model

Pick one painful workflow users already do in your app. "Find the right record and update status" or "Draft a reply from the ticket context". Measure before/after. Everything else flows from that.

The integration architecture that survives contact with users

Retrieval first (RAG over your DB + docs) — this is 60-70% of quality.
Tool definitions that are tiny, typed, and reversible where possible.
Context injection: pass the current record/screen the user is looking at.
Output sanitisation + confidence scoring + human escalation.
Observability: every call, retrieval and decision logged.

Never give the model blanket write access on day one.

Rollout without drama

Feature flag. Small internal or friendly cohort. Watch traces daily. Instrument cost per interaction. Only widen when evals show the quality bar is stable.

Common failure modes we see (and prevent)

Treating the existing schema as perfect for retrieval (it rarely is).
One giant prompt instead of narrow tools.
No evals until after launch.
Ignoring latency and token cost until users complain.

How we do it at Softgen

Most of our AI work is exactly this: taking a live product and adding agents, copilots or RAG that users actually rely on. We start with a Discovery Sprint (£4,950) that prototypes the core flow and gives a fixed price. AI features land from £18,000. We wire it into your stack (Next.js, Node, Python, whatever you run), add the evals and tracing, and ship behind a flag.

Have an app that needs the AI layer users expect in 2026? Send us a brief or run the numbers in the cost estimator first.

How to add LLM features to your existing app

The short answer

Start with the outcome, not the model

The integration architecture that survives contact with users

Rollout without drama

Common failure modes we see (and prevent)

How we do it at Softgen

Further reading

Quick answers.

Related guides.

How to build an AI agent for your product

AI agents for business: what they are, where they pay off, and what they cost

Model Context Protocol (MCP), explained

Let's build the thing.