We Run This Stack Today
Graphiti + FalkorDB (primary), Railway Postgres (mirror), LightRAG, and local Ollama models are all live on our own infrastructure. You are not paying for us to learn on your data.
Pilot · 4–6 Weeks
Knowledge Graph & LLM Pilot
Production-grade temporal knowledge graphs and RAG pipelines built on top of your existing CRM and ERP — without ripping out what you already have. Graphiti + FalkorDB + LightRAG + Ollama, deployed in 4 to 6 weeks.
4–6 weeks $15k–$40k
Graphiti shipped in late 2024. FalkorDB is a Redis-backed graph database that still feels like a secret weapon. LightRAG is a 2024 research project that's suddenly usable in production. In April 2026, fewer than 1,000 developers worldwide have any of these running in production.
9o4t Inc does. Today. We run Graphiti + FalkorDB as our primary memory layer, mirrored to Railway Postgres, with Ollama (llama3.2, mistral) serving local LLM inference and OpenAI handling structured-output tasks. It's not a pitch deck — it's the stack you'd be hiring us to build for you.
We run 4-to-6 week pilots that answer a specific business question: "Can a knowledge graph over our last seven years of Salesforce notes surface the pattern we've been missing?" At the end of the engagement you get a working system, a decision framework for productionizing it, and candid recommendations on what not to build.
WHAT YOU GET
- Production Graphiti knowledge graph with FalkorDB backend
- RAG pipeline integrating your CRM/ERP data with LLM responses
- Hybrid local-plus-cloud LLM deployment (Ollama + OpenAI)
- Ingestion pipelines from Salesforce, NetSuite, HubSpot, email, docs, or files
- Domain-specific entity and relationship extraction tuned to your data
- Executive demo + technical handoff documentation
- Build-vs-buy recommendations for the productionization phase
- Honest assessment of what the graph can't tell you yet
WHY 9o4t
First-Mover Pricing Window
Kubernetes consultants in 2015 charged $300–$500/hour because almost no one had production K8s experience. Snowflake consultants hit similar windows. Knowledge graphs are in that window right now — and the first-mover premium goes away inside 18 months.
Enterprise CRM Bridge
Most AI consultants can build RAG over a pile of PDFs. We can build RAG over Salesforce notes, NetSuite transactions, and HubSpot email threads, with the domain context of someone who's shipped SuiteScript for a decade. That bridge is rare.
Privacy-Conscious Deployment
Graphiti extracts entities via OpenAI (structured JSON mode). Embeddings run locally via Ollama's nomic-embed-text. For clients who can't send data to OpenAI, we can swap extraction to local Mistral and keep the entire stack on-prem.
IDEAL FOR
- Enterprises with years of unstructured data in Salesforce or NetSuite they can't search effectively
- Law firms, consultancies, and agencies wanting pattern recognition across case/project histories
- Mid-market companies exploring AI but afraid of 'consultant who read the docs last week'
- Tech-forward organizations wanting a privacy-first local LLM deployment (Ollama-based)
- CRM and ERP vendors building knowledge-graph add-ons or plugins
- Founders who need a defensible AI moat, not a ChatGPT wrapper
FAQ
What is Graphiti and why does it matter?+
Graphiti is an open-source temporal knowledge-graph library released in late 2024, optimized for LLM agents. It tracks how facts and relationships change over time — which is critical for enterprise use cases where 'who was the account owner in Q2' has a different answer than 'who is the account owner now.' Traditional RAG loses that context.
Why FalkorDB instead of Neo4j?+
FalkorDB is Redis-based, faster for the query patterns knowledge graphs hit (short, graph-local traversals), and dramatically cheaper to operate at mid-market scale. Neo4j remains excellent for analytics-heavy graph workloads — FalkorDB wins for agentic memory.
Do we have to give our data to OpenAI?+
No. The default Graphiti stack uses OpenAI only for entity extraction (requires structured JSON output), but we can swap in local Mistral for fully on-prem deployments. Embeddings already run locally via Ollama (nomic-embed-text). Many clients in regulated industries choose the all-local path.
What does a 4–6 week pilot actually deliver?+
A working system pointed at a real slice of your data (typically 6–12 months of CRM notes or documents), answering a specific business question you defined at kickoff. Plus a technical handoff and a recommendation: productionize, extend, or kill. We bias toward honest assessments over upsells.
How does this compare to just using ChatGPT or Claude?+
ChatGPT and Claude are excellent for one-shot reasoning, but they don't remember your account history across conversations and can't traverse your CRM graph. A knowledge graph gives the LLM a durable, queryable memory of your business — closer to how a senior employee carries institutional context.
What happens after the pilot?+
You own the code and the system. Options: take it in-house, hire 9o4t to productionize it (roadmap, hardening, SLAs — typical $50k–$150k annual retainer), or decide the use case doesn't justify the investment and walk away. We'll tell you which path is right.