Seven scopes, scoped tightly, delivered against named outcomes.

Agentic AI Architecture & Build

Outcome. A working, observable, evaluable agentic AI system in production on your stack, with a documented evaluation harness, cost guardrails, and a handover package your team can operate without us.

Scope. Architecture across LangGraph, CrewAI, AutoGen, PydanticAI, DSPy, Microsoft Semantic Kernel. Multi-agent orchestration, tool definition, planning and reflection loops, human-in-the-loop control planes, MCP and A2A protocol integration. Retrieval architecture across Pinecone, Qdrant, Weaviate, pgvector, FAISS, Milvus, Chroma. Reranking across ColBERT and Cohere. Observability across LangSmith, LangFuse, OpenTelemetry. Guardrails across NeMo Guardrails and Guardrails AI.

LLM Fine-Tuning & Alignment

Outcome. A fine-tuned, evaluated, and production-deployed model that meets your accuracy, cost, and latency targets, with a reproducible training and evaluation pipeline.

Scope. LoRA, QLoRA, and full-parameter PEFT. RLHF, DPO, PPO, and RLSF alignment. Cross-lingual transfer learning. RTL localisation. Token, cost, and latency optimisation. Prompt engineering and PromptOps. Constitutional AI where governance requires it.

RAG & Knowledge Fabric

Outcome. A governed enterprise retrieval system with hybrid search, semantic reranking, evaluation telemetry, and per-tenant isolation, sized to your corpus and query load.

Scope. Hybrid retrieval, GraphRAG, agentic RAG, chunking and indexing strategy, retrieval evaluation harness, vector database selection, governance integration with Collibra, Atlan, OpenMetadata, Microsoft Purview.

LLMOps & Production AI Platform

Outcome. A production-grade LLMOps stack with prompt versioning, regression evaluation, structured output validation, observability, cost guardrails, blue-green rollout, and automated rollback on evaluation regression.

Scope. MLflow, Weights and Biases, NVIDIA Triton, BentoML, ONNX Runtime, TorchServe, Kubeflow, ArgoCD, model versioning, drift detection, CI and CD for AI services.

Healthcare & Regulated AI

Outcome. A HIPAA and GDPR aligned healthcare AI system or compliance audit, delivered with documented evidence packs for procurement, security, and clinical governance.

Scope. Clinical agentic AI design, OpenFDA and equivalent data integration, AES-256 and TLS 1.3 secure delivery, SHA-256 audit logging, EU AI Act 2026 compliance review, OWASP LLM Top 10 hardening, zero-trust security for AI APIs.

Planetary AI & Remote Sensing

Outcome. A planetary or remote-sensing AI module benchmarked to Q1 publication standards and deployable on a Tesla T4 or equivalent inference target.

Scope. NASA and JPL HiRISE data pipelines, vision transformers, EfficientNetV2, ConvNeXt V2, Swin V2, YOLO v11, Faster R-CNN with ResNet-50-FPN, Mask R-CNN, Grad-CAM explainability, multi-modal fusion.

AI Strategy & Fractional Chief AI Officer

Outcome. A board-ready AI strategy with named projects, sized teams, capital plan, and quarterly milestones, optionally accompanied by ongoing fractional executive presence.

Scope. AI portfolio review, build-versus-buy guidance, vendor short-list construction, regulatory positioning under EU AI Act and ISO 42001, fractional executive operating cadence, hiring scorecards for AI roles.

Principal-led, end to end.

Every engagement is owned by Ruksh from discovery through production. When scale requires resources, a project team is assembled and guided under direct oversight, with full accountability for outcomes retained at the principal level. We do not subcontract senior engineering judgment.

Outcomes, not outputs.

Every engagement is measured against business outcomes: faster decisions, reduced operating cost, improved compliance posture, or a new capability the organisation could not previously execute. The evaluation harness is documented from week one.