Support copilots
Answer customer questions from your own help docs, product database, and order history — with citations, confidence scores, and a clean handoff to humans for the hard ones.
AI LAB · 01
Production-grade LLM applications
GenAI Integration that ships — not slide-deck demos.
We embed GPT, Claude, and Gemini into your products with retrieval-augmented generation, proper guardrails, and the boring infrastructure that keeps production AI from quietly degrading. Most clients see their first measurable win inside six weeks.
What we build
Six things we ship every quarter.
If you've seen ChatGPT and thought "this should be inside our product" — these are the most common ways we make that happen for clients.
Semantic search
Replace keyword search with embedding-based retrieval. Users find what they meant, not what they typed.
Drafting & summarisation
Auto-draft emails, contracts, meeting recaps, and reports — using your tone, your templates, and your data.
Internal developer copilots
Custom Cursor-style copilots fine-tuned on your codebase, internal libraries, and engineering conventions.
Multi-turn chat agents
Conversational interfaces with memory, tool use, and structured outputs — embedded inside your product UI or Slack.
Guardrailed workflows
PII redaction, prompt injection defence, hallucination checks, and human-in-the-loop review — for regulated industries.
Use cases
Where GenAI earns its keep.
A few real-world scenarios where LLM integration has paid for itself within months of going live.
Product Q&A on category pages
An LLM-powered shopping assistant that answers "Does this fit a 6-month-old?" or "Will it work with my iPhone 15?" by reading product specs, reviews, and Q&A — reducing pre-purchase support tickets by 60%.
−60%support tickets
+18%conversion rate
In-product onboarding copilot
A sidebar assistant that watches what the user is doing and answers contextual questions from documentation. New-user activation rates jump because nobody has to leave the product to read help docs.
+34%activation
−45%time to first value
Document review and Q&A
Upload a 200-page contract, lease, or financial filing and ask plain-English questions. Every answer cites the exact paragraph it came from — so reviewers verify in seconds, not hours.
10×review speed
100%citation rate
The stack we use
No religious wars — we pick what fits.
Model choice depends on accuracy, latency, cost, and data-residency requirements. Here's our working stack — but the right answer for your project might be different, and we'll tell you so.
Foundation models
- OpenAI GPT-4o, GPT-4 Turbo
- Anthropic Claude Sonnet, Opus
- Google Gemini Pro, Flash
- Meta Llama 3, Mistral, Qwen
Orchestration
- LangChain & LangGraph
- LlamaIndex
- Vercel AI SDK
- Model Context Protocol (MCP)
Vector stores & retrieval
- Pinecone, Weaviate
- pgvector, Qdrant
- Elasticsearch hybrid search
- Cohere, Voyage embeddings
Eval & observability
- LangSmith, Langfuse
- Braintrust, Helicone
- Custom eval harnesses
- Human-in-the-loop review
How we work
From discovery to production in six steps.
Most clients run a 2-week AI Discovery Sprint first — a fixed-price scoping engagement that produces a working prototype and a clear path to production.
01
Discovery sprint
Two weeks. We map the workflow, pick the model, and ship a working prototype against real data.
02
Data & retrieval
Connect knowledge sources, build the RAG layer, set up embeddings and reranking.
03
Prompts & eval
Iterate prompts against a real eval set. We never ship LLM features without measurable accuracy.
04
Guardrails
PII redaction, prompt-injection defence, output validation, and a fallback to humans where it matters.
05
Integration
Wire it into your product UI, internal tools, Slack, or wherever users actually live.
06
Monitor & iterate
Logging, cost tracking, drift alerts, and weekly tuning until the numbers settle.
Frequently asked
Questions we hear every week.
What is GenAI integration?
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GenAI integration is the process of embedding large language models such as GPT, Claude, or Gemini into existing business software and workflows. At Appsmediaz, we combine these models with retrieval-augmented generation (RAG), authentication, guardrails, and monitoring so the AI behaves reliably in production — not just in demos.
Which LLMs does Appsmediaz integrate?
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We integrate OpenAI GPT-4 and GPT-4o, Anthropic Claude (Sonnet and Opus), Google Gemini, Meta Llama, Mistral, and other open-source models. We help you pick the right model based on cost, latency, accuracy, and data residency requirements.
How long does a GenAI integration project take?
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A typical GenAI integration project takes 4 to 10 weeks. Simple chatbot or copilot integrations can ship in 4 to 6 weeks, while enterprise RAG systems with multi-source retrieval, evaluation pipelines, and human review take 8 to 12 weeks.
How much does GenAI integration cost?
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GenAI integration projects at Appsmediaz typically range from $10,000 for a focused chatbot or copilot to $50,000 or more for enterprise RAG platforms with custom retrievers, guardrails, and monitoring. We provide fixed quotes after a 30-minute discovery call.
Is my data safe when using LLMs?
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Yes. We use enterprise model endpoints (OpenAI Enterprise, Anthropic Claude for Work, Azure OpenAI) that don't train on your data. For sensitive workloads we deploy open-source models in your private cloud, add PII redaction, and implement role-based access controls.
Explore the rest of the AI Lab
Got an LLM use case in mind?
Book a free 30-minute call with a senior AI engineer. We'll tell you honestly whether GenAI fits, and what it would cost.
Schedule a call