Add AI to Your Existing SaaS Product in Weeks

LLM integration services for product teams. AI copilots, RAG pipelines, Claude and OpenAI features — built into what you already have. No rewrite. No six-month roadmap.

LLM Integration Services for SaaS Products

Your product already works. We add the AI layer.

Most SaaS teams don't need a new product. They need the one they have to get smarter — a copilot in the sidebar, search that understands questions, support that answers itself. That's what we build. Into your stack, on your data, in weeks.

Who we work with

Product teams that need to add AI to an existing SaaS without stalling the roadmap
Startups shipping their first LLM feature before the next funding round
Founders with a working product and a list of "AI" requests from users

What we build

AI copilots inside your SaaS — in the sidebar, in the editor, wherever your users work
RAG pipelines: LLMs that answer from your docs, tickets, and database, not from the internet
Claude and OpenAI integrations for support, onboarding, and internal ops
Starting from zero instead? That's our AI MVP service →

What We Do: LLM Integration, Copilots, RAG

Five things. We do them well and we don't pad the list.

AI Copilot Development for SaaS

A copilot your users actually open twice. We design the interaction, wire it to your data, handle context and memory, and ship it inside your existing UI. Not a chat bubble bolted on — a feature that belongs there.

LLM Integration into Your Existing Product

Add AI to your SaaS without touching what works. We build the LLM layer end to end — prompts, evals, guardrails, fallbacks — and connect it to your product: chat, search, analytics, CRM. Clean interfaces. Zero duct tape.

RAG Development Services

LLMs that know your business, not just the internet. We build retrieval pipelines over your docs, tickets, and databases — chunking, embeddings, vector search, reranking — so answers are grounded in your data. Already have one that hallucinates? We fix those too.

Claude & OpenAI Integration Developers on Demand

Need an OpenAI or Claude API integration developer this month, not next quarter? We join your team, take the LLM-heavy tickets, and unblock your engineers. Your repo, your process, our hands.

AI Strategy & Use-Case Discovery

Not sure which AI feature is worth building first? We'll tell you — including if the answer is "none yet." One-week discovery: we go through your product and user requests, map what an LLM can realistically do, and hand you a shortlist with effort and impact. Then you decide.

Models We Integrate: OpenAI, Claude, Llama, Bedrock

From copilots to custom ML — we've shipped it.

Need something specific? Pick from the list, or tell us what your users are asking for. Most of it starts with an LLM integration and ends with a feature they don't want to live without.

AI That Creates: Content, Ideas, Experiences

Smart Assistants for Teams & Users

Prediction & Optimization with Machine Learning

Text & Language Intelligence

Computer Vision That Powers Real Ops

Hyper-Personalized Experiences

Go from prompt to output — useful, on-brand, and instantly usable

1 Generate product descriptions, summaries, landing copy

2 AI image generation from idea or sketch

3 Agents that complete tasks & interact with APIs

4 Smart data parsers & summarizers

5 GPT-like chat interfaces for users

6 Support agents with memory & context

AI That Creates: Content, Ideas, Experiences

Build copilots that reduce manual effort and make work smoother.

1 Auto-reply bots for helpdesk / support

2 Workflow automation via natural language

3 Dispatch / scheduling assistants

4 Analytics insights via chat

5 Internal knowledge Q&A bots

6 Feedback & training copilots

Make smarter decisions — backed by data.

1 Forecasting (demand, churn, traffic, etc.)

2 Time series & trend modeling

3 Classification & smart labeling

4 Anomaly detection for ops or fraud

5 User scoring & probability modeling

6 Smart ranking & search

Prediction & Optimization with Machine Learning

Make sense of user input, feedback, docs, and conversations — in any format.

1 Conversational agents for customer service

2 Auto-translation & transcription tools

3 Trend spotting in messages & logs

4 Smart text categorization

5 Sentiment analysis for product/UX feedback

Automate what eyes can’t scale — from inventory to people to content.

1 Object recognition for logistics, retail & QC

2 Face-based access or verification

3 Real-time tracking of assets & people

4 Auto-tagging & categorization of images

5 AI-generated image captions or summaries

6 Upscaling and enhancement of images

Show the right thing to the right user — automatically.

1 Product & content recommendation systems

2 Personalized feeds, emails, dashboards

3 Ad optimization based on user signals

4 Real-time behavior-based adaptation

5 Multi-source filtering (collaborative + content-based)

6 Onboarding flows that adapt to intent

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How an LLM Integration Project Runs

Week 1: Audit & Scope

We read your codebase, look at your data, and talk to whoever owns the roadmap. Out of that comes a fixed scope: what the AI feature does, which model, what it costs to run per month. No surprises after this point.

Week 1-2: Data & Retrieval

If the feature needs your data — and it usually does — we build the retrieval layer first. Chunking, embeddings, vector store, access rules. The LLM only ever sees what that user is allowed to see.

Week 2-4: Build & Evaluate

Prompts, tool calls, guardrails, fallbacks. We write evals before we write the feature, so "it works" means something measurable. You see a demo every week and tell us where it's wrong.

Week 4-6: Ship Into Your Stack

The feature goes into your product, your repo, your CI. OpenAI, Claude, or Bedrock behind a clean interface, so switching models later is a config change, not a rewrite. Monitoring and cost tracking included.

After Launch: Watch & Improve

LLM features drift. Models get updated, users find edge cases, costs creep. We watch the evals, fix what breaks, and tune what's expensive. Retainer if you want us around, clean handoff if you don't.

Ready to add AI to your product?

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Why SaaS Teams Pick Lunqor for LLM Integration

15+ Years of Expertise

Ten years of shipping software before LLMs existed. We know what production means, and we don't confuse a demo with a feature.

Evals before features

Every LLM feature we ship has a test set behind it. "It seems to work" is not a status we report.

Commitment to Innovation

We use Claude, OpenAI, Llama, and Bedrock every day, and we pick the one that fits your data and budget — not the one with the loudest launch.

Global Availability

With offices in Ukraine and Poland and clients worldwide, we adapt to various time zones and hourly schedules. Whether you're in the U.S., Europe, or beyond, our team ensures seamless collaboration and AI software development support when you need it.

Enterprise-Grade Security & Compliance

We prioritize security and compliance in all data and AI services, implementing industry best practices for encryption, access control, and regulatory adherence. Our AI solutions are designed to protect sensitive business data while ensuring high performance and reliability.

Dedicated Partnership Approach

We've shipped our own SaaS products. We know what a bad integration costs you six months later, and we'd rather not be the reason.

FAQ

How do you add AI to an existing SaaS without a rewrite?

We build the AI layer as a separate service with a clean API and plug it into your product where it needs to show up — a sidebar, a search box, a background job. Your core codebase stays your core codebase. We've done this on Next.js, Rails, Django, Laravel, and a few things we'd rather not name.

Claude or OpenAI — which should we integrate?

Depends on the task. Claude tends to win on long documents, careful instructions, and anything where tone matters. OpenAI has the broader ecosystem and more tooling. Llama or Bedrock make sense when data can't leave your cloud. We build behind an abstraction so you can switch later — and we'll tell you which one we'd pick for your case on the first call.

What is RAG, and do we actually need it?

RAG (retrieval-augmented generation) means the LLM answers from your data — docs, tickets, database — instead of guessing from training. If your users ask questions that only your product can answer, you need it. If they just want text rewritten or summarized, you don't. We'll tell you which.

How long does an LLM integration take?

A single feature — a support copilot, semantic search, an assistant over your docs — is 2-4 weeks. Something with multiple tools, agents, or heavy data work is 4-8. We fix the timeline in week one and it doesn't move unless the scope does.

How much does it cost to add AI to a SaaS product?

A focused LLM feature starts in the low five figures. Bigger integrations with RAG, multiple models, or agents cost more, and we'll tell you the number before you commit. One thing people forget: we also estimate the monthly API bill, because a feature that costs $4,000 a month to run is a different decision than one that costs $200.

How do you ensure AI solutions are secure?

Your data goes to the model provider only when it has to, and only what that user is allowed to see. We use enterprise API tiers with zero data retention, keep keys server-side, and log every call. GDPR by default; HIPAA when you need it. And the double period in the old answer is gone too.

Have an AI feature your users keep asking for?

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