Apps Horizon

AI Engineering & Agents

AI agent and LLM development, built for production

AI that holds up in production, not just in demos.

Overview

Apps Horizon builds AI agents, copilots and voice assistants that do real work inside your product: answering from your own data, calling your APIs and handing off to a person when they should. We work across Claude, Gemini and OpenAI models and choose per task, weighing accuracy, latency and cost rather than defaulting to one vendor.

Most AI features look good in a demo and fall apart in production. The difference is engineering: retrieval that returns the right context, tools with clear contracts, evaluations that catch regressions before users do, and guardrails around what the model may say and do. That is the part we focus on, for startups and product teams in the Chandigarh Tricity, across India and worldwide.

What’s included

  • AI Agents & Copilots

    Agents that plan and act through your APIs, and in-product copilots that draft, summarise and answer, with a person in the loop where it matters.

  • RAG & Knowledge Search

    Retrieval-augmented generation over your documents, tickets and databases, with chunking, embeddings and ranking tuned so answers cite the right source.

  • MCP Servers & Tool Use

    Model Context Protocol servers and typed tool definitions that let Claude and other models use your systems safely.

  • Evals & Guardrails

    Test sets and automated evaluations for accuracy and tone, plus input and output guardrails, so every prompt or model change is measured before it ships.

Who it’s for

  • SaaS products adding an AI assistant or copilot for their users
  • Teams with a large knowledge base that staff search every day
  • Operations-heavy businesses automating repetitive, multi-step workflows
  • Founders who need to prove an AI feature works before funding a full build

How an engagement runs

  1. 01

    Use case and data review

    We map the workflow, the data the model needs and what a good answer looks like, and agree how success will be measured.

  2. 02

    Prototype and evaluate

    A working prototype on your real data, scored against an evaluation set, so model and retrieval choices rest on results rather than guesses.

  3. 03

    Take it to production

    Integration with your product, auth and APIs, plus guardrails, logging, cost controls and fallbacks for when a model is slow or wrong.

  4. 04

    Monitor and improve

    Evaluations keep running after launch, so quality is tracked as models, prompts and your data change.

Technologies we use

  • Claude API
  • MCP
  • RAG
  • Voice agents

See the full stack on our Technology page.

AI Engineering: common questions

We build on Claude (Anthropic), Gemini (Google) and OpenAI models, and can use open-weight models when data must stay inside your own infrastructure. The model is chosen per use case after testing on your data, and the architecture keeps it swappable.

Every engagement starts with an NDA. We use the providers’ business APIs, which do not train on your data by default, keep customer data out of prompts where it is not needed, and can deploy inside your own cloud account when compliance requires it.

Yes, and that is the most common request. We work inside your existing codebase and infrastructure, adding agents, copilots or knowledge search behind your current authentication and APIs.

Other services

Let’s build it together

Tell us about your project. We typically reply within 24 hours, and every project starts with a 30-minute discovery call.

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