Rekap gives every team member an AI Chief of Staff.
It listens. It remembers. It moves the work forward.
It’s not another tool. It’s infrastructure for execution.
Resumes and gut feelings won’t cut it—AI ensures you hire based on real signals, not guesswork. Our Rolodex tracks every candidate interaction, while Agent Workflows analyze interviews, rank applicants, and automate follow-ups to keep your pipeline moving.
Your best customers deserve more than scattered notes and forgotten follow-ups. REKAP gives you a single pane of glass into every conversation, decision, and next step—so you can deliver white-glove relationship management at scale, without anything slipping through the cracks.
Great managers don’t just manage—they elevate.
REKAP transforms every conversation into a coaching opportunity, surfacing insights from 1:1s, meetings, and feedback loops so you can develop talent in real time, not just during performance reviews.
Scaling isn’t about working harder—it’s about knowing where to focus. REKAP connects the dots across your pipeline, team performance, and customer interactions—surfacing hidden opportunities, reducing bottlenecks, and automating next steps so you can grow faster, with precision, not guesswork.
Your AI-Powered Relationship Hub of who’s who, what they need, and what to do next.
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These AI-native Power Tools think, adapt, and execute, building structured processes from messy conversations, notes, and meetings.
REKAP does not develop or train its own foundational language models. Instead, we adopt a model-agnostic architecture, meaning we work with best-in-class third-party models from OpenAI, Anthropic, Google, and others.
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Our system acts as a context engine, using Retrieval-Augmented Generation (RAG) to inject accurate, relevant context into these models based on structured and unstructured data. The tool design includes:
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- No exposure of sensitive user data to model training pipelines.
- No classification or predictive modeling based on demographic, biometric, or protected class attributes unless explicitly part of the customer’s input context.
- An internal governance layer that logs all prompts, completions, and automation triggers—ensuring transparency and traceability.
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Because we don’t build our own LLMs, we don’t use proxy features for protected characteristics unless such data is explicitly included by the customer in the task or prompt context (e.g., DEI analysis tasks).
We rely on the foundational model providers to conduct rigorous bias and safety evaluations. Here's how we manage this:
- Customers can select their preferred model provider, enabling them to make choices aligned with their compliance needs.
- We surface and log the model’s decisions and outputs, which can be reviewed in context via our audit tooling.
- We provide optional configuration to inject bias mitigation prompts or use tuned chains, especially in recruiting or decision-support contexts.
For model-specific bias research, you can review the public safety and evaluation documentation from:
- OpenAI Model Cards
- Anthropic’s Claude Safety Documentation
- Google’s Gemini Model Ethics Docs
REKAP offers an exportable audit trail for every AI operation, including:Inputs and outputs for each interaction
- Timestamps and user context
- Model/provider used
- Embedded memory objects and instructions
This creates an end-to-end AI audit log, which can be reviewed in real time or exported for compliance, QA, or bias evaluation.
To clarify: REKAP itself does not train foundational models. Our system facilitates orchestration, not training. The data used to inform the models’ behavior during task execution is injected in real-time via our RAG architecture. This includes:
- Internal customer data (meeting notes, spreadsheets, ATS records, etc.)
- Public web data or internal wikis, when authorized
- Pre-built rubrics and workflows designed by your team or REKAP
The base model training is handled by providers like OpenAI, Anthropic, or Google. Each of them publishes information about the scope, safety, and representativeness of their training datasets.
Transform your teams into builders—unlock intelligence, automation, and impact to drive faster decisions, better execution, and stay ahead in the new era of value creation.