LANGUAGE MODELSTHAT WORK IN YOURREAL ENVIRONMENT
From RAG architecture to deployed agent — production generative AI grounded in your data.
The gap between a compelling GenAI demo and a production system customers trust is an engineering problem. Ankh builds RAG pipelines that retrieve accurately from 500,000-document corpora, fine-tunes LLMs on proprietary data without catastrophic forgetting, deploys self-hosted models where data cannot touch third-party APIs, and orchestrates multi-agent workflows that complete multi-step industrial processes reliably — with rigorous requirement definition, systematic evaluation, and production-grade deployment practices.
What We Build
RAG Pipeline Development
Document ingestion and chunking, embedding model selection (sentence-transformers, text-embedding-3, domain-fine-tuned), vector database configuration (Pinecone, Weaviate, pgvector), hybrid dense+BM25 retrieval, cross-encoder re-ranking, and context assembly within the LLM context window.
LLM Fine-Tuning (LoRA, QLoRA, Full Fine-Tune)
Parameter-efficient fine-tuning with LoRA and QLoRA on curated datasets to instill domain-specific behavior without degrading general capabilities — plus full fine-tuning with catastrophic forgetting regularization and evaluation against task and general benchmarks.
AI Agent Development & Orchestration
AI agents using LangChain, LlamaIndex, CrewAI, and AutoGen — with task decomposition, explicit tool error handling, planning architectures (ReAct, Reflexion, tree-of-thought), and testing against adversarial inputs and tool failure scenarios.
Voice AI Pipeline Development
Streaming ASR (Whisper, distil-Whisper, Azure STT), LLM dialogue management, and streaming TTS (ElevenLabs, XTTS v2, Kokoro) — with voice activity detection, turn-taking logic, and backend tool-use for real-time conversational applications.
Multimodal AI Integration
Multimodal pipelines using GPT-4V, Claude 3, LLaVA, and Idefics for visual inspection reports, document understanding, defect classification, and field technician support — with image pre-processing, prompt construction, and structured output parsing.
Self-Hosted & Privacy-First LLM Deployment
Ollama for development and vLLM (paged KV cache) for high-throughput production on customer-controlled infrastructure — with 4-bit or 8-bit GGUF quantization and Kubernetes scaling.
Prompt Engineering & Evaluation Frameworks
Prompt libraries using chain-of-thought, few-shot exemplars, and constitutional prompting — with LLM-as-judge evaluation pipelines (RAGAS for RAG, custom harnesses for agents) and regression test suites that catch prompt regressions across model versions.
Generative AI for Industrial Applications
Industrial GenAI with confidence scoring, human review workflows for safety-critical outputs, and full audit logging — integrating with CMMS, ERP, and DCS for inspection reports, maintenance work orders, and operator alert explanation.
AI Compliance & Governance Frameworks
AI governance documentation, audit trail infrastructure for LLM interactions, and human-in-the-loop workflows aligned to EU AI Act risk classification, NIST AI RMF, and FDA AI/ML-Based SaMD guidance.
How We Work
Use Case Definition & Feasibility Assessment
Vaguely defined use cases — not inadequate technology — are the primary cause of GenAI project failure. Feasibility assessment produces a written specification covering task definition, evaluation criteria, data availability, regulatory exposure, and recommended technical approach before any code is written.
Data Audit & Knowledge Base Preparation
Data quality determines the ceiling of RAG and fine-tuning performance — coverage, format diversity, recency, and contradictory content are audited before ingestion pipeline design. Chunking strategy, metadata extraction, and embedding model selection are determined by document structure and domain characteristics.
Architecture Design & Prototype
Retrieval strategy, context assembly, prompt structure, and output parsing must be validated before production, not discovered in it. A full-path prototype on representative inputs is delivered in 2–4 weeks; precision@k, recall@k, MRR, and failure mode data directly inform the production architecture.
Evaluation Framework Construction
The evaluation pipeline is built before production: a golden QA dataset, RAGAS metrics (context precision, recall, faithfulness, answer relevance), LLM-as-judge scoring, and latency benchmarks under load. The framework runs in CI and blocks any deployment that regresses a defined quality metric below threshold.
Production Implementation & Integration
Production implementation covers authentication, rate limiting, streaming response handling, conversation history, cost monitoring, and graceful degradation for LLM API outages. Self-hosted deployments add GPU provisioning, vLLM tuning (tensor parallelism, batch size, KV cache), and Kubernetes autoscaling manifests.
Deployment, Monitoring & Iteration
Monitoring covers token cost, retrieval and generation latency, hallucination rate via consistency sampling, and retrieval drift as the corpus evolves. Monthly reviews assess whether metrics have held and whether user feedback warrants a prompt update, retrieval tuning, or fine-tuning cycle.
Engineering the Reliability Gap Between Demo and Production
RAG Architecture: Beyond Naive Top-K Retrieval
Naive top-k cosine retrieval fails at unusual phrasings and technical terminology. Hybrid dense+BM25 with Reciprocal Rank Fusion, cross-encoder re-ranking of top-50 candidates, and query transformation (HyDE, multi-query decomposition) address vocabulary mismatch — validated against precision@k, recall@k, and MRR on a held-out evaluation set.
API Platform DevelopmentLoRA and QLoRA: Fine-Tuning LLMs on Proprietary Data Without GPU Clusters
LoRA trains only low-rank decomposition matrices (1000x fewer parameters than full fine-tuning); QLoRA extends this to 4-bit NF4 base models, enabling 13B/34B fine-tuning on a single 24 GB GPU. Instruction-tuning datasets of 1,000–5,000 curated examples are constructed; the merged adapter is benchmarked on MMLU and HellaSwag to detect general capability regression.
AI/ML DevelopmentAgentic Workflows: From Tool Use to Reliable Multi-Step Automation
ReAct handles sequential tasks with logged scratchpad reasoning; CrewAI or AutoGen orchestrates multi-agent workflows with explicit dependency graphs. Every tool interface includes validation, timeout handling, and LLM-formatted error messages tested against adversarial inputs so agents hold on difficult cases, not just average ones.
Software Development
From RAG architecture to deployed agent — production generative AI grounded in your data.
Generative AI Across Ankh's Product Range
Generative AI augments hardware products with natural language interfaces, automated reporting, and intelligent decision support — turning sensor data and operational logs into actionable intelligence.
AI Vision Systems
Vision-language model integration for natural language defect description and automated inspection report generation
View ProductIndustrial Equipment Monitoring
LLM-powered maintenance work order generation from sensor anomaly data with CMMS API integration
View ProductMedical Devices
Clinical decision support with RAG over medical literature and patient records under HIPAA-compliant self-hosted LLM deployment
View ProductPOS & Kiosk Hardware
Voice AI kiosk interfaces with streaming ASR/TTS and conversational order management
View ProductStructural Monitoring
Automated structural health reports from sensor time-series with natural language summaries for non-engineer stakeholders
View ProductCold Chain Monitoring
Regulatory deviation reports from temperature excursion data for pharmaceutical cold chain compliance documentation
View ProductRuggedized Devices
Field technician AI assistant with RAG over service manuals, wiring diagrams, and fault code databases for offline voice queries
View ProductData Loggers
Natural language query interface over measurement archives enabling non-technical users to extract insights without SQL
View ProductSmart Home Automation
Conversational home automation agent with function-calling interfaces to device control APIs and natural language scenes
View ProductWireless Communication
Network configuration assistant with RAG over device documentation and LLM-generated config validation for complex RF deployments
View ProductHardware Product Context Shapes Our AI Approach
Intermittent connectivity, data sovereignty prohibiting cloud APIs, and FDA-auditable AI output require architectural decisions most GenAI providers have never faced. Every decision — self-hosted LLM deployment, structured output enforcement, legacy backend integration — is shaped by these constraints from the first design session.
Evaluation-Driven Development, Not Vibe-Driven Development
The evaluation framework is built before the system — 'good' defined quantitatively, golden dataset constructed for real-world and adversarial queries — so architecture decisions are data-driven, not impression-driven.
Production Infrastructure, Not Notebook Prototypes
Production GenAI requires API design, authentication, rate limiting, streaming response handling, cost monitoring, and CI/CD discipline. Ankh delivers systems version-controlled, containerized, tested, and monitored — with runbooks, not just README files.
Deliverables
Production Application Codebase
RAG pipeline, agent orchestration, API layer, database schemas, and deployment configuration — production-standard with type annotations and test coverage.
Vector Database & Knowledge Base
Configured vector database with ingestion pipeline, embedding model config, metadata schema, index optimization, and update scripts.
Fine-Tuned Model & Adapter Weights
LoRA adapter weights or full checkpoint, training configuration, dataset documentation, benchmark evaluation results, and deployment artifacts for the target inference framework.
Evaluation Framework & Golden Dataset
Golden Q&A dataset, automated RAGAS or custom scoring pipeline, CI regression integration, and baseline performance report across all evaluation dimensions.
Prompt Library & System Documentation
Versioned prompt library with rationale documentation, chain-of-thought examples, and guidelines for adapting prompts to new use cases.
Infrastructure Deployment Package
Docker Compose for dev, Kubernetes for production, infrastructure-as-code, CI/CD pipeline, monitoring dashboards, and LLM token cost tracking.
AI Governance Documentation
System card, query/response audit logging with retention policy, and compliance checklist against NIST AI RMF, EU AI Act, and FDA SaMD guidance.
Operational Runbook
Architecture diagram, monitoring alert procedures, model update and retraining procedures, vector database maintenance, and cost optimization recommendations.
Ankh delivers production generative AI systems with complete source code, evaluation infrastructure, operational runbooks, and the documentation required for regulatory review in sensitive application domains.
Often Paired With Generative AI
AI/ML Development
GenAI applications incorporating computer vision or time-series prediction require the full ML engineering stack for perception and prediction components.
ExploreAPI Platform Development
GenAI intelligence reaches web frontends, mobile apps, and backend systems through the API platform Ankh's platform practice designs and implements.
ExploreSoftware Development
AI-powered features live inside larger software products — Ankh's software development practice provides the application framework, UI, and backend business logic.
ExploreEdge Data Pipelines
GenAI applications processing hardware sensor data require robust edge pipelines for ingestion, transformation, and routing to the AI inference layer.
ExploreWork That Demonstrates the Standard
Industrial Maintenance Platform: LLM-Powered Work Order Generation from Sensor Anomalies
Ankh built a RAG-augmented LLM pipeline combining sensor anomaly data with retrieval from 8,000 service manuals to generate structured CMMS work orders. Against 500 expert-written golden orders, urgency classification matched 91% and corrective action overlap was 87%; mean time-to-dispatch fell 67% and triage workload fell 3 hours per shift.
Read the Case StudyHealthcare SaaS: HIPAA-Compliant Clinical Note Assistant with Self-Hosted LLM
Ankh deployed a self-hosted vLLM stack with LoRA-fine-tuned Llama-3-8B and RAG retrieving from the patient longitudinal record in pgvector — keeping all PHI off third-party APIs. In a 90-day pilot across 12 providers, documentation time per encounter decreased 58% and burden scores improved 2.3 points on a 7-point scale.
Read the Case StudyReady to Build a Generative AI System That Works in Production?Past the demo — into the deployment.
Ankh brings evaluation frameworks, error handling, monitoring, and the engineering rigor that separates production generative AI from a prototype — start with a use case scoping session to define success criteria.