PineHive
The AI-native casework platform for controlled expert workflows.
Turn communication, documents, prior work and approved knowledge into review-ready expert deliverables—with source grounding, human approval and traceability built into the workflow.
AI prepares. Experts review. PineHive remembers.
More than a case list, document archive or AI chat.
Traditional systems digitise status, files and administration. General AI tools generate text, but they do not provide the case context, permissions, review structure or accountable approval required for expert delivery. PineHive is the production layer between source material and reviewed work.
| Aspect | Traditional case tools | Generic AI chat | PineHive |
|---|---|---|---|
| Primary focus | Tracks cases and files | Generates responses | Structures the end-to-end case workflow |
| Preparation | Limited preparation support | Weak organisational context | Case-aware retrieval and source grounding |
| Review | Review often happens elsewhere | No accountable approval | Human review and approval in the workflow |
| Knowledge reuse | Knowledge is difficult to reuse | Conversation memory is not governance | Controlled reuse of approved knowledge |
From source material to reviewed delivery.
PineHive is being designed around the complete lifecycle of repeatable expert casework.
- 1
Intake and case setup
Communication, documents, responsibilities and case requirements are collected and structured.
- 2
Understand and retrieve
Relevant material, missing information and approved knowledge from prior work are identified within the correct permission context.
- 3
AI-assisted preparation
Source-grounded AI prepares summaries, questions, work memos and structured draft material.
- 4
Expert review and approval
Consultants, reviewers and approvers verify sources, resolve uncertainties and retain professional responsibility.
- 5
Delivery and organisational memory
Approved work is delivered, recorded and made reusable for future cases under controlled access.
Product areas
What PineHive is being built to support.
Practical controls that general-purpose AI tools and traditional case systems often leave disconnected.
Case-centred workspace
Bring source material, communication, status, preparation and decisions into one case context.
Case-aware retrieval
Retrieve information within the correct organisation, client, case, permission and workflow context.
Source-grounded preparation
Prepare work products with visible sources, uncertainties and required human checks.
Human review and approval
Make review, escalation and accountable approval part of the delivery workflow.
Audit and traceability
Record sources, AI runs, workflow versions, changes, reviews and final decisions.
Reusable approved knowledge
Turn reviewed outputs and completed cases into controlled organisational memory.
Built around clear human responsibility.
AI assists preparation. People retain professional accountability at each stage.
Case manager
Oversees flow, ownership, status and blockers.
Consultant or specialist
Prepares and verifies the expert work.
Reviewer
Checks sources, assumptions, completeness and quality.
Approver
Takes accountable responsibility for final approval.
Client participant
Supplies material, answers questions and follows agreed progress.
Controlled AI, retrieval and data handling.
PineHive retrieves information within the correct organisation, client, case and permission context. AI classifies, summarises and drafts while people verify, correct, escalate and approve—with traceability across sources, AI runs and final decisions.
- Visible source references and uncertainty flags
- Permission-aware retrieval within case context
- Human review and accountable approval in the workflow
- Audit trail across sources, AI runs and decisions
- Works above existing document environments
- Connectors introduced progressively as validated
PineHive-managed AI
Managed AI services within the PineHive workflow layer.
Approved private AI endpoints
Private endpoints approved by the organisation for sensitive casework.
Customer-controlled model deployments
Model deployments operated within the customer environment.
Built for repeatable expert workflows.
- Financial and operational reviews
- Compliance and regulatory assessments
- Due diligence
- Technical and quality assessments
- Procurement and supplier evaluations
- Insurance and claims-related casework
- Contract and document reviews
- Other repeatable professional-service workflows
Current phase
Focused development and workflow validation.
PineHive is in active development around a limited number of repeatable, document-heavy workflows. The immediate goal is to prove a shorter path from source material to review-ready work—improving source coverage, review clarity and reuse of approved knowledge. Integrations, private endpoints and broader deployment options follow where real workflows justify them.
Hyperity is interested in speaking with a small number of organisations that want to evaluate a controlled AI-assisted production workflow using neutral, well-defined pilot boundaries.
Discuss a design partnership