Architectural Comparison

Zdravo vs Engram

Two different bets on AI memory. Both solve the “brilliant stranger” problem. Only one is auditable, deployable in minutes, and compliance-native.

What Both Products Agree On

Organizational AI memory is the next infrastructure layer
Context reconstruction is expensive and wasteful
Enterprises need AI that knows their organization
Token costs are a growing concern for AI deployment

Where We Differ

Dimension	Engram	Zdravo
Architecture	Learned memory — fine-tunes models on your data	Structured memory — knowledge graph + semantic search
Deployment time	Weeks (requires per-org model training)	Minutes (connect and start saving memories)
Compute requirements	GPU infrastructure required (Azure, custom clusters)	No GPU required. Works with existing infrastructure.
Auditability	Model weights hold knowledge (black box)	Explicit knowledge graph — every memory, every retrieval logged
Update frequency	Requires model retraining to incorporate new knowledge	Memories update in real-time as your team works
Data privacy	Org data used in model training — GDPR/HIPAA complexity	Your data never trains models. Retrieval-only. PII detected on ingestion.
Model dependency	Tied to Engram's proprietary models	Model-agnostic — swap LLMs, keep your memory layer
Vendor lock-in	High (retraining required to migrate)	Low (standard APIs, export anytime, open data format)

Which Approach Is Right for You?

Choose Engram if you:

Want maximum inference-time performance and are willing to wait for training
Have a GPU compute budget and dedicated ML team
Don't need audit logs or compliance documentation
Are comfortable with a black-box model approach

Choose Zdravo if you:

Need compliance (SOC 2, GDPR, HIPAA) — retrieval-only, no model training on data
Want real-time memory updates without retraining
Need full auditability — every memory, every retrieval, logged
Want to start today without a GPU budget or ML team
Prefer model-agnostic infrastructure that won't lock you in

The Compliance Story

Model-training approaches like Engram's face a fundamental tension with data privacy regulations. Training a model on organization-specific data means that data becomes part of model weights — making it difficult to prove what was learned, impossible to selectively erase specific data, and complex to audit for regulatory compliance.

Zdravo's retrieval-based architecture sidesteps this entirely. Your data stays in your database. PII is detected and flagged on ingestion. Memories are encrypted at rest. Every access is logged in a tamper-evident audit chain. When an employee leaves, their knowledge transfers cleanly. When a regulator asks “what does your AI know?” you can show them the exact graph.

In regulated industries — healthcare, legal, finance, government — the retrieval-based, audit-friendly approach wins almost by default. You cannot train a model on PHI and call it compliant.

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