Hakase Drug Lifecycle OS
Discovery · Clinical · Regulatory · Medical Affairs · Commercial · Care

The AI-native operating system for the drug lifecycle.

The whole pharma value chain on a single, HAIOps-governed AI engine. One engine you build once and reuse everywhere; a defensible in-silico wedge that predicts what your lab will measure; and a spine of products that spans discovery through care.

01
The moat
Engine — one core engine you build once and reuse everywhere.
02
The wedge
Wedge — Hakase-native in-silico stack, led by the BioDigital Twin cascade, lab-confirmed.
07
The surfaces
Surfaces — owned products covering every stage, discovery to care.
The platform

Three layers, one platform.

Not a suite of tools stitched together. A single engine, a defensible wedge, and the product surfaces that sit on top of both. Read it from the foundation up.

03
Surfaces
Seven product surfaces
Purpose-built layers on one core engine.
Hakase BioDigital Twin  ·  Hakase Clinical  ·  Impakt Clinical  ·  RegXtrakt  ·  Impakt Care  ·  Hakase GPT  ·  aktiv8
02
Wedge
Hakase BioDigital Twin
Run the experiment before you run it — an in-silico cascade, lab-confirmed.
01
Engine · the moat
Hakase Engine
One orchestration layer · one shared RAG flywheel · governed by HAIOps.
↑  Read from the foundation up
Layer 01 — The moat

One core engine

Hakase is not a suite of tools stitched together. Every module runs on a single core engine — sharing one orchestration layer and one RAG flywheel — with every output passed through an evidence gate and cross-cutting audit and evaluation before it reaches a user.

Inputs are drawn from public sources. We never read, store, or use your proprietary data to train models for anyone else.

That's the moat: full tenant isolation, enforced by architecture.

Single core engine
One orchestration layer and one RAG flywheel shared across every module — not siloed, one-per-product.
Evidence gate on every output
Cross-cutting audit and evaluation metrics vet every answer before it reaches a user.
Full tenant isolation
Public-source inputs only. Your proprietary data never trains models for anyone else — enforced by architecture.
Healthcare AI Operations

The operating layer for safe, traceable, clinically-governed AI.

HAIOps extends traditional MLOps with clinical validation, patient-safety oversight, regulatory traceability, and operational governance for healthcare AI systems.

01
Clinical Validation
02
Regulatory Traceability
03
Safety Surveillance
04
Explainability
05
Confidence Scoring
06
Human Oversight
Governance & trust

Built for a regulated industry.

Every advancement is gated, every claim is provenance-tracked, and privacy holds by construction — so in-silico evidence can be presented in the language regulators already use.

Falsifiable, not confident
Outputs are ranked on an evidence ladder — literature → in-house → class-median — and actively attacked by falsifiers. Convergence of two independent methods clears the gate, not a single confidence score.
Human in the loop, by mandate
A reviewer gate is required before any progression to animal or clinical work. Hakase structures and accelerates expert judgment — it does not remove it.
Privacy is structural
No customer program data ever trains the engine. Tenant isolation and data_class tagging are enforced at write time — a guarantee by construction, not by promise.
Regulator-legible evidence
The validation program is designed to engage ASME V&V40, MID3, FDA ISTAND and EMA qualification pathways, so in-silico evidence lands in frameworks regulators already use.
Layer 02 — The wedge · Bio. Pre-clinical

Hakase BioDigital Twin

Run the experiment before you run the experiment.

Predict your IND-ready handoff in silico — before you fund the research. Lab-confirmed, not lab-replacement.

A candidate molecule enters four cascading in-silico layers — from in-vitro to first-in-human dose gating — each returning a GO / Watch / No-Go verdict. Real lab budget only touches the candidates that clear all four.

The cascade Go Watch No-go
01
Cellular-scale AI simulation
Layer 1 — in-silico ADME, safety & activity
02
Structural + tissue cross-validation
Layer 2 — two streams converge below Δdiv
03
Organism-level PBPK
Layer 3 — scales to whole-organism PK
04
First-in-human dose gating
The IND-ready handoff
How the wedge pays off
Run it first
Four cascading in-silico layers
Every candidate is screened from in-vitro to first-in-human before any lab spend.
Verdict at each gate
GO / Watch / No-Go
Each layer returns a verdict; only molecules that clear all four proceed.
The handoff
IND-ready, lab-confirmed
Real lab budget touches only the candidates that survive the full cascade.
Layer 03 — The surfaces

Purpose-built layers on one core engine.

On the shared engine, the products stop looking like scatter and start looking like coverage — one stage feeding the next, from first signal to the clinic, the label, and back to the bedside. These are the internal R&D and conversational surfaces — aktiv8, the market-facing intelligence layer, spans the whole lifecycle and gets its own section below.

Hakase-native — built on the engine Powered by Hakase — branded products on the same engine
01
Discovery · Pre-clinical
Hakase BioDigital Twin
Hakase-native
Run the experiment before you run it — a four-layer in-silico cascade to an IND-ready handoff. This is the wedge.
02
Clinical intelligence
Hakase Clinical
Hakase-native
Clinical intelligence for real-time trials. The Hakase Bridge imports trial configs from BioDigital Twin Layer 4, fused with ClinicalTrials.gov, FAERS, PubMed & OpenFDA.
03
Clinical operations
Impakt Clinical
Powered by Hakase
The software that actually runs the trial — EDC, ePRO and CDISC — powered by the Hakase engine.
04
Regulatory
RegXtrakt
Powered by Hakase
A live regulatory command centre for GCC & MENA — registrations, obligations, submissions, bilingual translation, mapped to SFDA, MOHAP & MOPH.
05
Care
Impakt Care
Powered by Hakase
The Care Pathway Intelligence Loop: real-world evidence from the bedside flows back upstream, sharpening the next molecule, the next trial, and the next label.
06
Cross-cutting
Hakase GPT
Hakase-native
The conversational surface of the core engine, present in every module — and now the Medical Affairs coworker too, turning your evidence into cited scientific narrative for KOL & MSL teams. Reasons over your records, never the open internet or another tenant's data.
Partner-powered
BLACKCOMB — Diagen
On the roadmap
CMC · Quality
Powered by Hakase · The front door & data flywheel

aktiv8 — the intelligence layer for everyone around the science.

aktiv8 is a Life Sciences Intelligence & Connection Platform. It identifies, tracks and contextualises activity across the entire ecosystem — and connects the right stakeholders to act on it, regardless of role.

It doesn't manage the science itself. It gives everyone around it — investors, BD teams, CROs, CDMOs, banks, advisors, vendors — the intelligence to find opportunities and reach the right counterpart at exactly the right moment.

The signal-to-connection platform for life sciences — it monitors every company, asset, catalyst, trial, funding event and regulatory milestone across the lifecycle, and connects the right people to act, with verified contacts and AI-drafted outreach, before the opportunity is widely known.
Coverage across the full drug lifecycle
Discovery
Preprints, patents, first IND filings
License early, before competition drives up the price.
Phase 1 / 2
Readouts, Series A/B, active investors
In-license post-readout, co-develop, or win the CRO before the RFP.
Phase 3 → NDA
Live PDUFA calendar, precedent data
Co-promotion, distribution, or regulatory tooling — a narrow window.
Launch
M&A signals, verified contacts
Acquire, distribute, co-promote or supply at peak need.
Post-market
Label extensions, biosimilar mapping
Partner on a new indication or track a competitor's programme.
In practice

What the platform does in practice.

Real engagements on the shared engine — cross-stage synthesis that would take a human analyst weeks, delivered in a session.

Asset valuation
One molecule, many indications
For a single-molecule biotech, Hakase mapped the asset across multiple indications and a manufacturing carve-out — assembling a sum-of-parts thesis from public evidence and its own modeling in a fraction of the usual time.
Pharmacovigilance
RegXtrakt — signal detection at speed
Disproportionality statistics (PRR, ROR, χ²) computed live, with Bayesian and AI-assisted assessment routed to a reviewer — one shared signal-detection layer powering safety intelligence across every surface.
Business development
aktiv8 — landscape intelligence
Across a corpus of companies, catalysts, trials and investors, aktiv8 surfaces competitive moves, white-space and licensing angles — turning a multi-day landscape exercise into a same-session analysis.

Case studies describe real engagements and shipped capabilities. Quantitative outcomes per engagement available under NDA.

Engagement model

Decision-analytics-as-a-service.

Delivered as outcomes, not seats — priced to the unit of value you actually buy.

Per-deliverable
A single in-silico package, dossier or analysis
Fastest route to first value
Ideal for a one-off decision
Per-stage
A full lifecycle stage — e.g. a pre-clinical work-up
Continuous predict-then-confirm loop
Ideal for an active program
Per-trial · platform
Engine embedded across a program or portfolio
Cross-stage context compounding
Ideal for enterprise & partners

One platform for the entire drug lifecycle.

Discovery through care, on a single HAIOps-governed engine — built once, and reused across every stage. That's the moat, the wedge, and the spine of products, together.