Production AI systems, built around your proprietary data.
Enterprise Retrieval-Augmented Generation (RAG), multi-modal LLM workflows, and autonomous reasoning agents engineered with guardrails, low latency, and zero hallucination tolerance.
A raw OpenAI wrapper is a toy. Enterprise AI requires rigorous engineering.
Most AI implementations fail in production because they are naive wrappers around commercial APIs: they hallucinate answers, rack up catastrophic token bills, leak confidential data, and break when models update.
I engineer robust, production-grade AI systems. By combining hybrid vector search (Pinecone / pgvector) with dense re-ranking, semantic caching, and strict deterministic evaluation suites, your system returns provably accurate answers grounded exclusively in your data.
Whether deploying autonomous multi-agent workflows that trigger real API tools or intelligent recommendation engines like ShopSphere AI, I build AI that delivers measurable ROI.
Semantic Cache & Guardrail Proxy
Filters adversarial injections, caches common vector hits, and prunes tokens.
Hybrid Retrieval & Reranker
Combines BM25 keyword search with dense vector embeddings for 99%+ recall.
Agent Reasoning & Function Calling
Executes deterministic database queries, validates outputs with Zod schemas.
Continuous Evaluation & Evals
Continuous automated benchmarking for faithfulness, answer relevance, and safety.
What's included in AI engineering
End-to-end artificial intelligence pipelines from unstructured data ingestion to autonomous agent deployment.
Enterprise RAG & Semantic Search
Ingest PDFs, databases, and Notion docs into hybrid vector stores with intelligent chunking and precision reranking.
- Hybrid Dense & Sparse Search
- Cohere Cross-Encoder Reranking
- Context Window Compression
Autonomous Agent Workflows
Multi-agent systems equipped with deterministic tools to query internal databases, schedule calls, and trigger external APIs.
- Function & Tool Calling
- Multi-Step ReAct Loops
- Zod Schema Output Validation
Semantic Caching & Cost Reduction
Slash token bills by up to 70% using Redis semantic similarity caching and prompt compression algorithms.
- Vector Distance Caching
- Prompt Token Pruning
- Cost & Budget Telemetry
Hallucination Defense & Guardrails
Rigorous input sanitization and output guardrails to prevent prompt injections, toxicity, and unauthorized data leakage.
- Adversarial Injection Filtering
- Deterministic Grounding Checks
- PII Redaction Engine
Fine-Tuning & Model Distillation
Fine-tune open-weight models (Llama 3, Mistral) on your specialized domain datasets for private, cost-effective inference.
- LoRA / QLoRA Fine-Tuning
- Domain Dataset Curation
- Private Self-Hosted Inference
Streaming UI & Copilot Interfaces
Sleek generative AI interfaces with chunk streaming, citation footnotes, code highlighting, and user feedback loops.
- SSE Token Streaming
- Inline Source Citations
- Thumbs Up/Down Evals
What we engineer for AI applications
Practical AI workflows engineered to automate manual work and unlock proprietary knowledge.
Build fast. Deploy it right. See how we deliver.
A disciplined engineering path from data curation to production LLM deployment.
Data Audit & Grounding Strategy
Analyze source data formats, test embedding models, define chunking strategies, and establish evaluation metrics.
- ›Data Ingestion & Chunking Architecture
- ›Golden Benchmark Test Dataset
- ›Embedding Model Selection Matrix
Vector Ingestion & Agent Integration
Build automated ingestion pipelines, configure vector databases, implement RAG reranking, and wire agent tool calling.
- ›Hybrid Vector Store (pgvector / Pinecone)
- ›Agent Tool Calling Service
- ›Streaming API Microservice
Evals, Guardrails & Cost Tuning
Run automated evaluation suites with Ragas, test prompt injections, configure semantic caching, and tune token usage.
- ›Faithfulness & Accuracy Eval Report
- ›Prompt Injection Defense Verification
- ›Semantic Cache Configuration
Copilot UI Rollout & Telemetry Dashboard
Deliver responsive frontend interfaces with token streaming, citation badges, and LangSmith/OpenTelemetry monitoring.
- ›Production Copilot Web Interface
- ›Observability & Token Telemetry Dashboard
- ›Documentation & Maintenance Guide
Why Jimish for AI Engineering?
Real software architecture applied to AI models instead of superficial script wrappers.
| Evaluation Criteria | With Jimish (Direct Architect) | Traditional Agency | In-House Hiring |
|---|---|---|---|
| Implementation Strategy | Hybrid search, cross-encoder reranking, and strict Zod validation schemas | Basic LangChain script calling GPT-4 with zero caching or guardrails | Requires scarce, expensive ML engineers |
| Hallucination Defense | Multi-step citation verification and deterministic grounding checks | Hopes the prompt prevents hallucinations without verification | Requires developing custom evaluation benchmarks |
| Cost Optimization | Semantic caching and context pruning to cut token bills by up to 65% | Sends entire document dumps to expensive frontier models | Cost optimization usually ignored until bill shocks occur |
| Data Privacy & Governance | Zero-retention enterprise endpoints with option for self-hosted models | Unchecked use of public API endpoints without compliance | High internal security review hurdles |
| Speed to Production | Functional RAG prototype in 10 days; production-ready agent in 4-6 weeks | 3-4 month research experiments with no working software | 6+ months hiring and model exploration |
Built for teams with no room for error
Real-world AI systems built for mission-critical production environments.
ShopSphere AI
AI-Driven E-Commerce Platform with vector personalized recommendations.
Vantly Health
HIPAA-compliant care management platform automating family updates and staff workflows for nursing homes.
Engineering Principles & Deliverables
Deterministic Grounding
LLMs are never asked to guess facts. Every answer is strictly grounded in retrieved document chunks with visible source citations.
- Hybrid Sparse & Dense Retrieval
- Cross-Encoder Re-Ranking
- Exact Quote Citation Verification
Strict Structured Outputs
All LLM tool calls and generated payloads are parsed through Zod schemas. If the model generates invalid JSON, it self-corrects.
- Zod Schema Type Contracts
- Automated JSON Repair Loops
- Predictable Downstream API Calls
Cost & Latency Telemetry
Full visibility into every token consumed, cache hit ratio, and model latency with automated budget circuit breakers.
- Redis Semantic Caching
- Token Usage & Spend Alerts
- Distributed LLM Trace Logging
Built with technologies you trust
The premier modern AI engineering stack for high-throughput, accurate systems.
LLM Orchestration
Vector Databases
Embeddings & Reranking
Evaluation & Monitoring
Common Questions.
Everything you need to know about partnering for AI Engineering & Autonomous Systems. Have a unique technical challenge? Let's discuss it directly.
I enforce a strict Retrieval-Augmented Generation (RAG) architecture. The model is given a strict system prompt forbidding it from answering from general training weights. If the retrieved context chunks do not contain the answer, it returns a deterministic 'Information not found' response. Furthermore, outputs are cross-checked against source citations.
Ready to build high-scale software for your business?
Let's discuss your product goals, timeline, and technical architecture. Direct senior engineer access from day one.