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AI & LLM Integration

AI-Powered Solutions for Business Growth

We have seen the same mistake a hundred times: AI bolted to a product as a chatbot wrapper that hallucinates, has no memory, and no observability. We build AI differently — as a service with latency budgets, deterministic output validation, cost guardrails, and a feedback loop that makes models measurably better over time.

AI & LLM Integration — Production AI Systems
<10s
LLM Latency
Optimised with streaming and caching
90%+
RAG Accuracy
Measured against domain-specific evaluation set
60%
Cost Reduction
Via model routing and semantic caching
100%
Audit Trail
Every AI decision logged with full context
How we work

Our Methodology

Every engagement follows the same rigorous process — Agile delivery with TDD, contract testing, and observability built in from day one.

01

AI Feasibility & ROI Scoping

Before writing a prompt, we define the measurable success criteria, latency budget, cost ceiling, and fallback strategy.

  • Use-case prioritisation matrix
  • Model cost modelling
  • Latency budget definition
  • Baseline accuracy benchmarking
02

RAG Architecture Design

We design the document chunking strategy, embedding model selection, hybrid search configuration, and retrieval re-ranking pipeline.

  • Chunking strategy evaluation
  • Embedding model benchmarking
  • Hybrid search (BM25 + dense)
  • Metadata filtering design
03

Prompt Engineering & Evaluation Harness

Every prompt is versioned and evaluated against a golden dataset using RAGAS or custom metrics before shipping.

  • Prompt versioning (LangSmith)
  • RAGAS evaluation pipeline
  • Hallucination detection
  • Output schema validation (Instructor)
04

Observability & Cost Control

Token cost dashboards, latency P95 alerting, and automated red-teaming run continuously in production.

  • LangSmith / Langfuse tracing
  • Token cost Prometheus metrics
  • PII redaction middleware
  • Automated adversarial probing
Technology

Tech Stack

Battle-tested tools chosen for production reliability — not for the conference talks.

GPT-4oGemini 3.5 FlashPythonFlaskFastAPIDjangoRedisSocketNumpyPyTorchHuggingFaceKafka
Why us

Why Work With YakshLabs

9 years of lead engineering across 20+ enterprise systems. We bring architecture expertise, production scars, and a delivery process that makes outcomes predictable — not hopeful.

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We Use RAGAS — Not Vibes

Our AI systems ship with a formal evaluation harness. Faithfulness, answer relevancy, and context precision are measured on every deployment.

Model-Agnostic Architecture

We build abstraction layers that let you switch from GPT-4o to Claude to an open-source model without rewriting business logic.

AI Safety is an Engineering Discipline

Output validation, PII scrubbing, jailbreak detection, and rate limiting are built into the service layer — not the prompt.

What You Get

Every engagement delivers concrete, ownable artefacts — not just a working system.

Production RAG pipeline with evaluation harness
AI cost tracking dashboard
Prompt versioning system
LangSmith / Langfuse tracing setup
Guardrail middleware
Model performance baseline report

Ready to build AI-Powered Solutions?

Book a free 30-minute strategy session. We'll review your requirements and give you an honest technical assessment — no boilerplate proposals.