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Applied AI & Vector SystemsSpecialized Generative & Assessment AI Practice

AI & Data Systems Engineering (Production RAG)

Deterministic, zero-hallucination AI pipelines, vector retrieval architectures, and LLM rate-limit management.

Practice Overview

We turn raw LLMs and embeddings into production systems with deterministic guardrails, verified source grounding, and predictable operating costs. We specialize in high-stakes domains (education, legal, fintech) where hallucinations and out-of-context answers are unacceptable.

Core Engineering Capabilities

  • Deterministic RAG Engines: Retrieval-Augmented Generation pipelines linking every generated response to verified source citations.
  • Vector Database Architecture: Chunking strategies, semantic indexing, and high-dimensional search using Pinecone and Qdrant.
  • Cognitive Taxonomy Balancing: Programmatic enforcement of Bloom's Taxonomy and curriculum standards across assessment models.
  • Cost Governance & Budget Caps: Token-bucket rate limiting, semantic caching of LLM responses, and prompt optimization.
  • Multi-Modal Assessment Pipelines: KaTeX mathematical formula rendering and bilingual font rendering (Devanagari script).

Primary Technologies

Python FastAPIGoogle Gemini AIPineconeQdrantOpenAI APIRedis CacheZod / PydanticKaTeX

Industry Best Practices Guaranteed on Every Project

We uphold rigorous engineering standards across architecture, security, automation, and code cleanliness on every engagement:

Deterministic Ground-Truth Citation Enforcement

Every statement or question generated by the engine must link to verified chapter and page numbers from official curriculum textbooks.

Prompt Injection Defense & Schema Validation

Strict input sanitization, prompt separation, and Pydantic/Zod schema enforcement on all LLM JSON output streams.

Hybrid Retrieval (Lexical + Dense Vector)

Combining BM25 keyword matching with dense vector embeddings and cross-encoder re-ranking for superior retrieval precision.

Token-Bucket Budget Governance & Latency Caching

Strict rate limits per user/IP, caching frequent query embeddings in Redis, and avoiding redundant LLM API invocations.

Automated Hallucination Regression Testing

Continuous testing of prompt revisions against curated golden evaluation datasets before deployment to production.

Graceful Fallback Strategies

Deterministic fallback to rule-based templates or secondary model tiers when primary model APIs experience latency spikes or outages.

Verified Project Proof Point

Flagship Platform: Vidyanetra RAG PipelineRead Vidyanetra RAG Case Study →

A 90-second exam generation engine balancing cognitive levels and referencing NCERT textbooks with zero hallucinations.

Discuss Your Architecture Requirements

Have a technical product to build, a distributed backend to scale, or an existing architecture requiring an expert review? Reach out directly.