Five engines. One governed foundation. Everything institutional intelligence has to do, in one place.
The Datahive platform is organised as five composable intelligence engines. Each is production-grade on its own; together they form a single institutional AI stack — knowledge, documents, prediction, data, and governance — that runs inside your network and stays there.
Five engines, composed from top to bottom.
Platform engines
Knowledge Intelligence Engine turns that passive archive into a living, queryable infrastructure. On-premise LLM with production-grade High Availability, paired with an enterprise RAG pipeline that sources every answer from official documents. Inference is distributed across GPU nodes with auto-scaling, quantization, and automatic failover. Everything runs inside the institution's network — no data leaves.
Distributed LLM Inference
LLM inference spread across GPU nodes with auto-scaling and automatic failover.
GPU Cluster Management
Scheduling, quantization, and health monitoring across the full GPU fleet.
Model Optimization Pipeline
Compression, quantization, and throughput tuning for production workloads.
Model Lifecycle Management
Versioned weights, staged promotion, and safe rollbacks across environments.
Enterprise RAG Pipeline
Every answer grounded in your institution's own documents — no external calls.
Knowledge Graph & Semantic Indexing
Vector and graph retrieval across policies, archives, and reports.
Multi-Format Mass Ingestion
PDFs, scans, office docs, and database extracts — normalised and indexed together.
Integration Readiness
Push structured output straight into your downstream systems — no glue code.
Document Intelligence Engine automates the entire pipeline. Scans, phone photos, PDFs — anything incoming is classified, entities are extracted, the data is validated and routed to the destination system. OCR hits up to 99% accuracy on clean Indonesian text and stays reliable on imperfect scans. Not just OCR — a context-aware pipeline that reads, understands, and knows where things belong.
Up to 99% Indonesian OCR Accuracy
Trained on Indonesian typography — clean, scanned, and phone-photo inputs.
Intelligent Document Classification
Context-aware routing so every document lands where it belongs.
Named Entity Recognition
Extract people, organisations, amounts, and dates from unstructured text.
ID Document Recognition
Pre-trained extractors for national IDs and standard institutional forms.
Parallel Batch Processing
Thousands of pages per hour, horizontally scaled across the cluster.
Layered Data Validation
Cross-check extracted data against rules and reference sets before it's stored.
Integration Readiness
Push structured output straight into your downstream systems — no glue code.
Most institutions already have the data — it's just scattered, slow to access, and served as reports about what already happened. Predictive Intelligence Engine turns historical and operational data into forward-looking insight. It models where demand will spike, where budgets are anomalous, and which operational risks will escalate — combining unstructured context from narrative reports and public complaints with structured numbers.
Time Series Forecasting
Demand, revenue, and capacity predictions with confidence bands you can defend.
Real-Time Anomaly Detection
Flag outliers as they happen — not in next month's reporting cycle.
Predictive Demand Modeling
Anticipate where resources will be needed before it's too late to move them.
Clustering & Segmentation
Group customers, cases, or transactions by behaviour patterns.
Early Warning System
Triggers on leading indicators, not lagging ones.
Model Performance Monitoring
Drift, accuracy, and fairness tracked continuously — not quarterly.
EDW Integration
Read from and write back to your existing enterprise data warehouse.
Interactive Dashboards
Decision-ready views, not read-only reports.
An interoperability layer connects the existing ERP, correspondence systems, portals, and legacy databases to the DataHive ecosystem. The result is two permanent institutional assets: a base model whose value grows with more data — not a revocable license — and a unified data foundation that outlives DataHive itself.
Domain Dataset Curation
Curate and version your institution's own training corpus.
Policy Hierarchy Fine-tuning
The base model learns your policy structure and internal vocabulary.
Instruction Alignment Pipeline
Supervised fine-tuning on tasks that match your actual operations.
Continuous Alignment
Periodic re-training as your data and policies evolve.
Iterative Evaluation
Benchmarks tied to real institutional tasks, not generic leaderboards.
Every AI response is validated against source documents before delivery. Every interaction is recorded in an immutable audit log — unchangeable even by administrators. Access is RBAC-bound, and compliance reports generate automatically. Governance isn't a feature bolted on — it runs alongside the entire ecosystem from day one, so every deployment operates within defined, accountable limits.
Immutable Audit Logging
Write-once audit trail — unchangeable, even by administrators.
Hallucination Detection & Blocking
Outputs validated against source citations before they reach the user.
Dataset Traceability
Every inference traceable back to the training data that shaped it.
Role-Based Access Control
Granular permissions across users, datasets, and model capabilities.
Policy-Aware Filtering
PII and sensitive content redacted inline, according to policy.
SIEM Integration Readiness
Stream events to your security operations platform of choice.
Automated Compliance Reporting
UU PDP, audit, and regulatory reports generated automatically.
Isolated Deployment Architecture
Air-gapped, on-premise, under your own infrastructure control.
The platform is one thing. Getting it to production is another.
Three service tiers wrap around the engines — so you're not handed a stack and left to figure out the rest.
Advisory & Architecture
Scope the engagement with our senior engineers. Output is an executable blueprint — target architecture, risk register, and a signed scope-of-work.
- Current-state assessment
- Target architecture design
- Regulatory gap analysis
- Procurement & rollout plan
- Decision brief for leadership
Platform Implementation
From zero to first production use-case. Install, integrate, train, and co-build with your engineers — so your team owns it when we leave.
- On-premise install & HA
- Source system integration
- Security & compliance hardening
- First use-case to production
- Your team, trained & enabled
Managed Operations
Operational support with named engineers — not a ticket queue. Patch cadence, capacity planning, and incident response against real SLAs.
- L2 / L3 support
- Named on-call engineer
- Patch & version management
- Quarterly architecture review
- Incident post-mortem & RCA
A predictable path to first production case.
No multi-year program. No open-ended discovery phase. A fixed cadence you can defend to leadership.
Discovery workshops
Source system inventory, data owner interviews, regulatory mapping, and architecture whiteboarding with your leads.
Blueprint & sign-off
Target architecture, network topology, security controls, and acceptance test plan — reviewed with your team, signed by your sponsor.
Platform install & integration
Hardware provisioning, on-premise install, HA configuration, secrets management, and integration with the first source systems.
First use-case development
Feature pipelines, model training, serving endpoints, and dashboards — built jointly with your team on the new substrate.
Production cutover & handover
Acceptance tests green, runbooks signed, on-call established. Your team takes the pager; we're available on SLA.
Start with one engine. Or all five. Up to you.
We size the engagement to the problem. Some teams start with just Document or Knowledge. Some want the whole stack from day one. Both are fine.