Your team uses AI. None of it shares context.
Reflect is the persistent memory layer that connects every AI tool your organization uses. One place your decisions, context, and institutional knowledge live - accessible to Claude, ChatGPT, Cursor, and any MCP-compatible tool, inside your own infrastructure.
Air-gapped deploy · HIPAA-oriented self-host · SOC 2 alignment in progress · No data training
A decision gets made on Monday. By Wednesday, none of your team's AI tools know about it.
Someone new joins. Their Claude has no idea what your team has built, decided, or ruled out. They spend their first month asking questions everyone else already answered - to an AI that has no memory of any of it.
Someone leaves. Everything they knew about how your systems work, what customers have said, what was tried and failed - gone. No AI tool on your team inherits it.
This is not a workflow problem. It is an infrastructure problem. Your AI tools are stateless by default. Reflect gives them a shared state.
How we work with enterprise teams
Self-serve works for individuals and small teams. Enterprise deployments need scoping, integration, and a defined success criteria. We offer two engagement paths.
Custom Integration
We integrate Reflect into your existing AI stack
Your team has existing tools, workflows, and data sources. We scope a time-bound engagement to connect Reflect to your environment - Jira, Confluence, Bitbucket, internal docs, or any other source - and deliver a production deployment. No open-ended retainer. Scoped, time-bound, ends with something running.
Good fit for: Teams with a specific AI workflow they want Reflect to power. Engineering orgs that want to move faster than building their own memory layer. Healthcare AI teams that need patient context to persist across sessions without touching PHI in third-party systems.
Talk to us about a custom integrationPilot Program
We deploy Reflect for your team and measure what changes
Start with one team - typically 5-15 users. We deploy a private instance on your infrastructure, seed it with your team's context, connect it to the AI tools you already use, and run a 30-60 day pilot with defined success criteria. At the end, you decide whether to expand or stop. No lock-in.
Good fit for: Teams evaluating whether persistent AI memory actually changes how their people work. CTOs and Heads of AI who want to see proof before committing to a production deployment. Organizations where broad rollout requires internal buy-in from multiple stakeholders.
Schedule a pilot scoping callBoth engagement types run on private infrastructure. Your data does not leave your network. Pilot pricing: talk to us about scope.
Built for regulated and AI-forward organizations
Healthcare
Teams building AI-assisted patient workflows
Patient context that persists across sessions, care team handoffs, and model changes - without routing PHI through third-party systems. Air-gapped deploy means your patient data stays inside your infrastructure.
Consulting
Teams running AI across multiple client engagements
Per-client memory isolation means your Claude session for one client never bleeds into another. Switch contexts instantly. Your team stops rebuilding context from scratch on every engagement.
Engineering
Engineering orgs where AI context fragments across tools
Architecture decisions, codebase context, project history - accessible in Claude, Cursor, and ChatGPT without re-explaining. New hires ramp faster. Knowledge doesn't walk out when someone leaves.
Your infrastructure, your boundary
Same product across every deployment model. Choose the boundary that fits your security requirements.
Hosted | Isolated Hosted | Self-Host | |
|---|---|---|---|
| Runs on | Reflect cloud | Dedicated instance | Your VPC / on-prem |
| Data residency | US multi-tenant | Region of choice | Your infrastructure |
| Network boundary | Public API | Isolated endpoint | Air-gapped capable |
| Model egress | Enabled | Configurable | Disabled by default |
| Auth | API keys + OAuth | SSO + API keys | SSO + API keys + OIDC |
| Audit trail | Standard | Extended | Full, queryable, exportable |
| Tenant isolation | Logical | Process-level | Physical |
Defense-in-depth by defaultEvery layer designed for regulated environments. Your security team gets complete oversight.
Authentication
Encryption
Audit Trail
Model Egress Control
Tenant Isolation
Compliance
When “just build it internally” isn't the answer
Most enterprises consider building this in-house. Most underestimate what that actually means: vector store maintenance, retrieval tuning, embedding updates, compliance audits, ongoing security review. Reflect absorbs all of it.
Read the build-vs-buy case- Vector store maintenance and embedding pipeline updates
- Retrieval tuning across teams, tools, and deployment environments
- Compliance audits and ongoing security review
- Integration maintenance as every AI vendor changes their API
What the first 90 days look like
Most Reflect enterprise engagements follow this rhythm. Your timeline may compress or extend based on procurement and security review.
- 01Week 1Walkthrough and scoping
30-minute call with your Champion, Security lead, and any technical stakeholders. We map your AI stack, the specific workflows you want memory to cover, and your deployment requirements. No demo theater. We want to understand whether Reflect is actually the right fit before we both commit time.
- 02Week 2Security review
We send your Security team our architecture documentation, encryption posture, audit trail capabilities, and deployment options. Most reviews take 5-10 business days. We answer questions in writing and on calls as needed. If your Security team rejects the proposal at this stage, we end the engagement cleanly - no pressure.
- 03Weeks 3-5Private instance pilot
We deploy a private instance on your infrastructure - cloud, isolated, or air-gapped, depending on your boundary. Your team uses Reflect for real workflows. Typically 5-15 users. We meet weekly to walk through usage, edge cases, and any friction. Success metrics defined upfront.
- 04Week 6Pilot review and LOI
We share usage data, surface insights from your team's actual workflows, and discuss what production rollout would look like. If both sides want to proceed, we sign a Letter of Intent and start drafting the annual contract.
- 05Weeks 7-10Contract and procurement
Annual or multi-year contract, billed upfront, Net 30. Your procurement team handles internal approvals. We provide whatever security documentation, references, or technical details your buying committee needs to close internally.
- 06Weeks 11-12Production rollout
Full deployment to your team. Dedicated support contact. SSO integration. Custom SLA. Ongoing account management with monthly check-ins for the first quarter, then quarterly thereafter.
Who's involved
Every enterprise engagement involves these three roles in some form. We adapt to your internal process.
Ready to stop rebuilding context from scratch?
We scope every enterprise engagement to your stack, your compliance requirements, and your team's actual workflows. Start with a 30-minute walkthrough.