AI integration services
Add AI to the software you already run.
You do not need a new product to benefit from AI. We add AI features to the website, app or internal system you already have: search that understands questions, answers grounded in your documents, automatic data extraction, and summaries your team actually reads.
We handle the hard parts around the model: connecting it securely to your data, choosing the right model for the job, testing its answers, and keeping cost and speed under control.
What you get
- AI inside your productNew AI features that feel native to your existing app or workflow.
- Answers from your dataGrounded in your documents and records, with sources, not guesses.
- Tested qualityAn evaluation set that measures accuracy before and after every change.
- Controlled costThe right model for each task, with caching and limits to keep bills predictable.
Where it helps
- LLM API integrationConnect OpenAI and other models to your backend safely, with rate limits and secrets kept on the server.
- Retrieval-augmented generation (RAG)Let users ask questions and get answers drawn from your own documents and knowledge base.
- Document search and Q&ASearch contracts, manuals or policies by meaning, not just keywords.
- Structured data extractionTurn emails, PDFs and forms into clean, validated fields your system can use.
- AI-assisted reportingPlain-language summaries of your numbers, with the numbers themselves calculated by code, not the AI.
- Smart search and recommendationsHelp users find the right product, article or record faster.
- Business system integrationsConnect AI features to your CRM, help desk, database or ERP.
- Model selection and evaluationCompare models on your real tasks for accuracy, speed and cost before you commit.
How we build it
- 01 DiscoverWe look at your product, users and data to find where AI genuinely helps.
- 02 DesignWe plan the integration: data flow, model choice, prompts, security and fallbacks.
- 03 BuildWe implement it in your codebase or alongside it, with an evaluation set to measure quality.
- 04 DeployWe release it behind sensible limits, with monitoring for quality, speed and cost.
- 05 ImproveWe review real usage and failures and tune prompts, retrieval and models over time.
AI isn't finished at launch. Every system we ship is evaluated and monitored in real use.
Safeguards, built in
- Secrets stay on the serverAPI keys and model calls run in your backend or secure functions, never in the browser.
- Numbers stay deterministicCalculations are done by code; the AI explains them and is not allowed to change them.
- Rate limits and fallbacksUsage limits protect your budget, and features degrade gracefully if a model is down.
- Measured qualityAnswers are checked against a test set before every release.
What we build with
AI and LLMsOpenAI API (GPT-4o runs in production in Vikku Labs' own products), structured outputs, prompt and evaluation pipelines; other models when a project needs them
AutomationAPI integrations, webhooks, scheduled jobs, transactional email, event-driven workflows
EngineeringReact, Next.js, TypeScript, Node.js, Python, PostgreSQL, MongoDB, Supabase, AWS, Vercel, Docker, React Native
See our AI in action: try the AI Visibility Score or the AI Project Planner, both running GPT-4o in production.
Frequently asked questions
Can you add AI to our existing app without rebuilding it?
Yes, that is the most common case. We add AI features through your existing backend or a small secure service next to it, and connect them to your current interface.
What is RAG and do we need it?
Retrieval-augmented generation means the AI first finds the relevant parts of your documents or data, then answers using only those. You need it whenever answers must be based on your own information rather than general knowledge.
Which AI model do you use?
We use OpenAI models in production today, including GPT-4o in Vikku Labs' own products. We choose models per task based on accuracy, speed, cost and data requirements, and can compare alternatives on your real examples.
How do you stop the AI from making things up?
By grounding answers in your data with retrieval, asking for structured outputs we can validate, keeping calculations in code, and testing against known answers. When the AI has no good source, it should say so.
How much does it cost?
It depends on scope: how many workflows, systems and users are involved, and how much accuracy and oversight the work needs. Every project starts with a free discovery call, and you get a written scope and estimate before anything is built. For a quick ballpark, try our free Project Estimator.
Is our data safe?
We use your data only for your project, give each system the minimum access it needs, and keep secrets on the server, never in the browser. By default, data sent through the OpenAI API is not used to train OpenAI's models. If your data is sensitive, we design around it from day one, for example by masking personal details before they reach a model.
Who owns the code and the system?
You do. You get full ownership of the source code, prompts, configuration and documentation, handed over in your own repository.
Do you support it after launch?
Yes. AI systems need monitoring: we track quality and cost, fix failures and improve prompts and workflows as real usage comes in. Most clients stay on a light monthly retainer after launch.
Related services
Tell us what you want AI to do.
Book a free AI discovery call. We'll tell you honestly whether AI is the right fit, and what the practical next step is.