AI & Agentic Systems

Why RAG matters: Most corporate knowledge lies in unstructured documents — contracts, internal wikis, tickets, emails. Traditional full-text search fails on semantic queries. RAG combines LLM power with precise data from your sources.

How we do it: We use hybrid retrieval (dense embeddings + sparse BM25), multi-stage re-ranking, and domain-specific chunk strategies. For each project, we test 3-5 chunk configurations on real queries and measure recall@k. We typically achieve 92-97% recall on top-10 results.

Architecture in practice: Ingestion pipeline → chunking (semantic splitting, not naive fixed-size) → embedding (model chosen by language and domain) → vector DB (Qdrant/Weaviate) + BM25 index → retrieval → re-ranking (cross-encoder) → LLM with citations. Every step is measured and debuggable.

Common mistakes we avoid: Naive chunking by 512 tokens (breaks context), missing re-ranking (precision drops 15-25%), no retrieval quality evaluation, ignoring metadata (date, author, document version). We’ve seen RAG systems with 60% accuracy — after optimization, we got them to 94%.

What is agent workflow: Unlike simple prompt → response, an agent plans a sequence of steps, calls tools (APIs, databases, files), evaluates results, and decides next actions. It’s a programmable worker with defined mandate.

Orchestration: We use graph-based orchestration (LangGraph, custom DAG engine), where each node is an isolated step with defined input, output, and error handling. Agent can call multiple tools in parallel, aggregate results, and decide based on business rules.

Security and control: Every tool-call is logged with full context. We define permission boundaries — agent can read from CRM, but writes require human approval. Kill-switch stops agent at any step. Escalation rules are configurable per use-case.

Real example: Invoice processing agent — receives PDF, extracts data (OCR + LLM), validates against order in ERP, checks duplicates, writes to accounting system, notifies accountant about discrepancies. Processes 200+ invoices/day with 98.5% accuracy. Humans handle only edge cases.

Why evaluation is critical: LLMs change (new model versions), data changes, user queries change. Without continuous evaluation, you don’t know if your system works — you only know it worked last month. We’ve seen systems where model upgrade degraded quality by 20% and nobody noticed for a week.

Our evaluation stack: Automated eval suites (golden dataset 200-500 pairs per use-case), LLM-as-judge for subjective quality, deterministic metrics (faithfulness, answer relevance, context precision), A/B testing for prompt changes. Everything runs in CI/CD — every deployment passes eval suite.

Production monitoring: We track latency (P50/P95/P99), token consumption, cost per query, error rate, retrieval quality (NDCG), user satisfaction (thumbs up/down + feedback loop). Alerts on anomalies — if accuracy drops below threshold, we know immediately.

What we measure and how: Faithfulness (answer is grounded in context), completeness (answer covers query), hallucination rate, toxicity, cost efficiency (cost per successful resolution). Dashboard with daily reports for stakeholders.

AI governance isn’t nice-to-have: In regulated industries (finance, healthcare, public sector), governance is deployment prerequisite. But even outside regulation — AI agent with production system access without governance is security risk.

What we implement: Role-based access control (who can do what), audit trail (every action logged with context), kill-switch (immediate agent stop), escalation rules (when to ask human), rate limiting, input/output guardrails, prompt injection detection, PII redaction.

Prompt injection protection: Multi-layered defense — input sanitization, system prompt hardening, output validation, canary tokens in context. We test every deployment against known attack vectors. No system is 100% secure, but we reduce risk by orders of magnitude.

Compliance and audit: We generate audit reports compatible with ISO 27001, SOC 2, GDPR. Every agent decision is reproducible — we log prompt, context, model response, tool calls. For regulated sectors, we implement model cards and AI impact assessment.

When to fine-tune: When prompt engineering isn’t enough (specific domain, output format, consistency), when you need to reduce latency/costs (smaller model = faster + cheaper), or when you need on-premise model (regulation, data residency).

Our approach: We start with analysis — do you really need fine-tuning, or is better prompting enough? If yes: collect training data (synthetic + real), fine-tune with LoRA/QLoRA, evaluate against baseline. We typically achieve 85-95% of GPT-4 quality with 10x smaller and 5x cheaper model.

Knowledge distillation: Large model (GPT-4, Claude) generates training data for smaller model (Llama 8B, Mistral 7B). Smaller model learns domain-specific behavior without massive datasets. Result: production model with <200ms latency and <$0.001 per query cost.

Inference optimization: Quantization (INT8/INT4), batching, KV-cache optimization, speculative decoding. For high-throughput scenarios (1000+ queries/min), we design inference stack with autoscaling and intelligent routing between models.

Why integration is critical: AI agent without connection to real systems is just a chatbot. Value emerges when agent reads from CRM, writes to ERP, creates tickets, sends notifications — when it’s part of the process, not just an add-on.

How we integrate: REST/GraphQL API adapters, webhook listeners, message queue consumers (RabbitMQ, Kafka), database connectors. Every integration has retry logic, circuit breaker, timeout handling, and dead letter queue. Monitoring at every connection level.

Typical integrations: SAP/ERP (invoices, orders, inventory), Salesforce/CRM (contacts, opportunities, activities), Jira/ServiceNow (tickets, incidents), Email/Teams/Slack (notifications, escalations), DMS (SharePoint, Confluence, internal wiki). We connect most within 1-2 weeks.

Change management: Technical integration is half the work. Other half is adoption — user training, gradual rollout (shadow mode → pilot → production), process impact measurement, iteration based on feedback. Without adoption, even the best AI system is useless.