A non-destructive intelligence layer that works with any business, any database, any AI model.
Joe writes "brk pds" in a ledger. Sarah types full sentences in Google Sheets. Enterprise AI demands clean, structured data — but real businesses don't work that way. You shouldn't have to change how you operate just to use AI.
Real-world databases range from 2-table free-text stores to 23-table fully normalized schemas. No single NL2SQL strategy, prompt template, or embedding model works across this spectrum. The router must adapt — deterministically.
Dave's been coming to your shop for 20 years. You know his truck, his kids, his preferred brake brand. That knowledge lives in your head. When you're gone, it's gone. AI should preserve it — not replace it.
LLMs are stateless. RAG retrieves documents, not relationships. MemoryForge maintains scored, weighted context graphs across sessions without model fine-tuning — enabling relationship-aware inference at query time.
Salesforce Einstein: $150/user/month. CDK Global: $50,000+ setup. ChatGPT: forgets everything between sessions. Small businesses deserve AI that remembers, adapts, and doesn't cost a fortune.
No database migration. No API rewrites. No vector DB provisioning. Mount MemoryForge as middleware between your existing data layer and any LLM. One config file. One launch script. Sub-millisecond routing overhead.
MemoryForge sits between your existing tools and any AI model. Nothing changes above or below. Everything gets smarter in the middle.
Every query is understood, remembered, and accounted for — with a full audit trail.
Ask questions in plain English. MemoryForge figures out what you mean, which database to query, and returns the answer — even if your data is messy or spread across systems.
Deterministic semantic routing maps natural language to SQL across heterogeneous schemas in 0.19ms. No embeddings, no vector search, no LLM in the critical path. Pure keyword-anchor-atom resolution.
Unlike ChatGPT, MemoryForge remembers your customers, their history, and the relationships that matter. Every interaction builds on the last — just like a real business relationship.
Persistent scored context graphs maintain weighted entity relationships across sessions. Memory scores decay, reinforce, and branch based on query patterns — without fine-tuning or retraining the underlying model.
Every answer comes with a receipt: what data was used, what was considered, and what wasn't. If AI influences a decision, you'll know exactly how and why.
Full counterfactual audit trail for every inference. Track which memory nodes influenced output, compute alternative paths, detect hallucination drift, and generate compliance-ready reports for EU AI Act and GDPR Article 22.
Each module works independently or together as a unified pipeline.
We built this because every existing option had a fatal compromise.
| MemoryForge | "Just Use ChatGPT" | Enterprise DMS | Build It Yourself | |
|---|---|---|---|---|
| Setup | One config file, one script | Copy & paste each time | 6-12 month integration | Months of dev work |
| Memory | Persistent across sessions | Forgets everything | Rigid CRM fields | You build & maintain it |
| Data Location | Stays on your machine | Sent to OpenAI servers | Vendor cloud | Depends on your choices |
| Cost | Free / self-hosted | $20/mo (no memory) | $50,000+ setup | Engineering salary |
| Accountability | Full audit trail | None | Basic logs | You build it |
| AI Model | Any model, swap anytime | GPT only | Vendor-locked | Whatever you integrate |
| MemoryForge | RAG Pipeline | LangChain Memory | Fine-Tuning | |
|---|---|---|---|---|
| Latency | 0.19ms routing | 200-800ms retrieval | 50-200ms | Same as base model |
| Memory Model | Scored context graph | Document chunks | Buffer / summary | Baked into weights |
| Scoring | Weighted decay & reinforcement | Cosine similarity | Recency only | N/A |
| Audit Trail | ✓ Full counterfactual | ✗ Source docs only | ✗ None | ✗ None |
| Counterfactual | ✓ Built-in | ✗ Not supported | ✗ Not supported | ✗ Not supported |
| Hallucination Detection | ✓ Drift scoring | ● Partial (grounding) | ✗ None | ✗ None |
| Model Lock-in | ✓ Model-agnostic | ● Embedding-dependent | ● LLM-dependent | ✗ Fully locked |
| Infrastructure Change | None (middleware) | Vector DB required | LangChain runtime | GPU training infra |
Your data never has to leave your building. But if you want it to, we support that too.
Run entirely on your own machine. SQLite, local LLM, zero network calls. Your data never leaves your hardware. Perfect for single-location businesses.
RecommendedConnect multiple locations over an encrypted mesh network. Same local-first architecture, accessible from anywhere your VPN reaches. No public internet exposure.
Multi-siteDeploy to your own cloud infrastructure — AWS, GCP, Azure, or any VPS. Full Docker support. You control the servers, the keys, and the data.
EnterpriseEvery piece of context used in an AI response is surfaced to the user. No hidden prompts. No invisible system instructions manipulating output. Full transparency by design.
Every memory node is scored, weighted, and tracked over time. You can see exactly how context influenced a response — and compute what would have changed without it.
The system presents information. It does not steer decisions. No recommendation engines. No engagement optimization. No dark patterns. The human decides — always.