What is RAG and when do you need it?

Retrieval Augmented Generation — a pattern where AI answers not from its training but from your base. Algorithm — vectorize documents, semantic search, pass relevant chunks as context. Needed when you have specific corporate info (docs, regulations, customer base) the model doesn't and shouldn't know.

pgvector vs Qdrant vs Pinecone vs Weaviate?

If you have Postgres and under 1M docs — pgvector (no extra service). 1M-100M docs — Qdrant (self-hosted, very fast). If managed — Pinecone (but pricey and cloud). Weaviate — good for hybrid keyword+vector filtering. We pick on audit based on volumes.

What's a multi-agent system and when do you need it?

When the task is too complex for one agent. Example — planner agent breaks user request into steps, researcher agent gathers data, writer agent composes answer, reviewer agent validates. We use LangGraph (great for complex state management) or CrewAI (simpler for role-based scenarios). Most businesses don't need this — one good agent with tools is enough.

How is reliability ensured in prod?

Four layers. First — retry with exponential backoff on transient errors. Second — fallback to backup model (e.g., GPT-4 → Claude → local Llama on outage). Third — idempotency keys on critical operations so retries don't duplicate. Fourth — Prometheus monitoring with Telegram alerts on latency or error rate spikes.

How much does the hosting cost?

Depends on load. Basic setup (n8n + Postgres + pgvector + Redis on one VPS) — from $15/month. With GPU for local model — from $300/month (A10/3090 rental). API inference cost — typically $0.01-0.10 per user request depending on model and length.

Do you do custom CRM integrations?

Yes, if there's any API (REST, GraphQL, SOAP, or even DB you can connect to). On the audit we review docs and estimate. Usually 2-4 days vs 1-2 for popular CRMs.

AI CRM Integration, RAG, Multi-Agent Systems on LangGraph

What’s in “integrations & infrastructure”

The most “engineering” service — what end users don’t see but without which any AI product collapses under real load. Includes:

Model connections — OpenAI, Anthropic, local via vLLM/Ollama; proper rate limiting, retries, fallbacks
RAG systems — vectorize your knowledge base, semantic retrieval, answers with source citations
Business system integrations — CRM (HubSpot, Salesforce, Pipedrive, ZohoCRM), ERP (NetSuite, Dynamics), messengers, email, calendar
Orchestration — multi-agent on LangGraph/CrewAI, task queues (Celery/Bull), state management
DevOps — VPS, Docker, monitoring, backups, CI/CD
Observability — structured logs, latency/error metrics, traces

RAG systems — our stack

Standard pipeline:

Ingestion — fetch documents from source (S3, Google Drive, Notion API, your CMS), parse (handling images, tables), chunk by meaning (500-1500 tokens with overlap)
Embeddings — vectorize via OpenAI text-embedding-3-large or local BGE-M3 (multilingual)
Storage — pgvector (up to 1M docs) or Qdrant (more, or production-load)
Retrieval — hybrid search — semantic + keyword (BM25), reranking via cross-encoder
Generation — LLM gets top-N chunks + prompt, generates answer with citations
Evaluation — precision/recall metrics on test set, regular refresh

Not magic — an engineering pipeline with dozens of tunable parameters specific to your task. Retrieval quality is the main factor in how well an AI agent works.

Multi-agent — when

90% of business tasks ship with one good agent + tools. Multi-agent is needed when:

Task requires parallelism — planner distributes subtasks to specialized executors
Needs role specialization — research agent + critic agent + writer agent on one document
Has complex workflow with states — human-approval here, retry there, fallback elsewhere

We use:

LangGraph — graph-based state machine, best for complex branching pipelines
CrewAI — simpler, for role-based scenarios
AutoGen — Microsoft, more powerful, harder in prod

Production-readiness checklist

Default inclusions:

Retry with backoff on all internal API calls
Fallback models — if GPT-4 down, switch to Claude, then local
Idempotency keys on critical ops (order creation, email send) so retries don’t dupe
Rate limiting on our service side (attack defense + accidental spend)
Structured logging — JSON logs with trace ID for step-by-step debugging
Health checks on all components + Telegram alerts on failures
Daily backups with 30-day retention, periodic restore tests

Monitoring

Baseline metrics:

API latency — p50, p95, p99 per endpoint
LLM cost — tokens and cost by model and scenario
Error rate — overall and by error type
Retrieval quality — semantic similarity between query and retrieved docs
User feedback — thumbs up/down on agent responses

All in Grafana with Telegram alerts on anomalies.

Get started

Book a free 2-day audit. We’ll review your AI plans, current stack, and decide whether to build from scratch or integrate into existing.

Integrations & Infrastructure

What’s in “integrations & infrastructure”

RAG systems — our stack

Multi-agent — when

Production-readiness checklist

Monitoring

Get started

What you get

Production-grade from day one

RAG over your knowledge base

CRM integration with anything

Multi-agent orchestration

How we work

Infrastructure audit · 2 days

Design · 2 days

Deployment · 5-6 days

Load test and handoff · 1 day

Tech stack

Pricing

Frequently asked

Book a 30-minute audit.