Retrieval-Augmented Generation (RAG) pipelines
ServicesAI & Data
Generative AI & LLM Integration
Generative AI & LLM Integration that earns its keep.
TeleMatrix Global's generative AI service builds RAG systems, copilots and domain-tuned assistants on large language models, inside your product or your company. The engineering targets the three things that sink these projects: leaked data, invented numbers and runaway inference bills.
rag / pipelineIllustrative
cited01 · Query
show me last quarter's revenue by region
02 · Retrieve
8 chunks · top score 0.87 · cite required
03 · Reason
claude-4.5-sonnet · 612t · 0.84s
answer
NAM $48.2M (+12%) [1] · EMEA $31.4M (+7%) [2] · APAC $22.1M (+19%) [1]
Always-on telemetryEval-first deliverySenior team only
Inside the engagement
What do you get when you hire TeleMatrix Global for generative ai & llm integration?
Ideal for
Teams putting LLMs in front of customers, or behind their own people.
Domain-tuned assistants & copilots
Vector search, hybrid retrieval, reranking
Streaming UX, citations, and refusal logic
Privacy controls, PII redaction, compliance evidence
Why TeleMatrix for this
Why do teams pick TeleMatrix Global for generative ai & llm integration?
RAG pipelines with citations, refusal logic, and PII redaction baked in from day one.
Domain-tuned assistants that respect your brand voice and policy constraints.
Streaming UX with progressive disclosure and source citations, not janky chat boxes.
Hybrid retrieval (BM25 + embeddings + reranking) tuned to your real questions.
Continuous evals against your real questions, not synthetic benchmarks.
Stack we ship with
Vendor-neutral. Senior in every layer of your stack.
A representative slice of the tools we use for generative ai & llm integration. We meet your platform where it lives, and we work with many more.
OpenAI GPT-4o
Anthropic Claude
LlamaIndex
LangChain
Pinecone
pgvector
Weaviate
Qdrant
Cohere Reranker
Hugging Face
Vercel AI SDK
Inngest
Datadog
Engagement timeline
How does a generative ai & llm integration engagement run?
A typical engagement looks like this. Faster, slower, or parallel tracks are all on the table when the work demands it.
Discovery
What questions, what sources, what answers must include or exclude.
Ingestion + retrieval
Build the pipeline, baseline retrieval metrics, calibrate chunking.
Generation + UX
Voice, citation requirements, refusal logic, streaming UI.
Eval + harden
Edge cases, PII scrubbing, guardrails, observability, load test.
How we run the work
Five phases. Same discipline, every engagement.
Discover
Goals, constraints, and the metric we're moving, locked in week one.
Plan
Architecture, scope, and a sprint plan you and your stakeholders can read.
Build
Senior teams ship in tight sprints. Demos every Friday, no surprises.
Launch
Hardening, eval, rollback plan, comms · a real launch, not a release note.
Operate
We measure, iterate, and keep the system improving long after handoff.
Outcomes
The targets we work towards.
Typical targets for generative ai & llm integration work, not guarantees. Your numbers will depend on starting point and scope. We agree the success metric in week one and report weekly against it.
- Business metrics, not vanity metrics. Reported weekly.
- Eval / QA gates every release.
- Access scoped to your engagement team, under your data policy.
95%+
citation accuracy on retrieved facts
-60%
time to find an answer vs search
4 to 6 weeks
to a working assistant
99.5%
refusal accuracy on out-of-scope queries
FAQ
Questions teams ask before they pick us.
Don't see your question? Email support@telematrixglobal.com or message us on WhatsApp.
Yes. Every answer can include source citations with deep links. We can require citations as a hard constraint.
AI · in production
Built to survive Monday morning.
Real systems, not demos. Eval harnesses, guardrails, latency budgets, and clear rollback paths.
Related capabilities
Pairs well with
Further reading
More on Generative AI & LLM Integration
From the journal
- RAG vs fine-tuning: retrieve the facts, tune the behaviourFor a company knowledge assistant, start with RAG so answers stay current and cited. Fine-tune only for behaviour your evals show prompts cannot fix.9 min read
- How to stop an AI chatbot making up answersChatbot hallucinations have five separate causes, from no grounding to a prompt that rewards guessing. Find yours, apply its fix, and measure every release.8 min read
- Your RAG pipeline is retrieving the wrong chunksWhen a retrieval system returns confident nonsense, the embedding model is rarely the problem. Chunk boundaries usually are. Here is how we diagnose and fix it.5 min read
Let's build
Ready to engineer the next chapter of your business?
Tell us where you are, where you want to go, and the deadlines you cannot miss. We'll respond within one business day with a clear next step.
Direct line
support@telematrixglobal.com
+91 79808 07674
Office hours
Mon to Sat · 09:00 to 19:00 IST
Support runs 24/7.