~2x
FASTER GROWTH
for organisations that invest consistently in research and innovation versus those that don't
dRSTi
We research what enterprises will run next. run next.
Explore · Validate · Transfer · Cultivate
The research and innovation unit of dbiz.ai. We take the hard, unproven parts of agentic AI, test them against real enterprise constraints, and hand over what survives: working technology, not a point of view.
The demo is rarely the problem. Data that isn’t ready, permissions that don’t hold, and evaluations nobody trusts are where enterprise AI stalls. dRSTi exists to solve those problems before delivery begins, so our clients aren’t the ones discovering them in production.
HOW WE ARE DIFFERENT
The unit runs on its own clock and its own funding, allowing long-horizon questions to be answered properly. Everything it produces is built to be handed to a delivery team or directly to you.
70%+
OF LABS UNDERDELIVER
corporate innovation labs fail to produce real business impact — usually because nothing transfers
70/20/10
PORTFOLIO BALANCE
core, emerging, and long-term bets funded together so today’s delivery and tomorrow’s options both advance
01
Explore
We track where the technology is actually going and frame the questions worth answering, not the ones easiest to demo.
02
Validate
Prototypes and experiments against real data, real permissions, and real cost envelopes. Evidence decides what continues.
03
Transfer
What holds up moves into our platforms and delivery teams, or into your stack, with the people and IP attached.
04
Cultivate
We grow the people, methods, and partnerships that make the next answer faster to reach than the last one.
Every gate is a decision, taken in the open
At each gate the evidence is reviewed against criteria agreed up front: technical, commercial, and strategic. Partners and clients sit in the reviews for the work they fund.
AT EVERY GATE · ONE OF FOUR CALLS
Go
fund the next stage
Kill
stop & reallocate
Hold
pause for now
Recycle
rework & return
CLOSE TO THE WORK
Protected funding and time, but never an ivory tower: every question comes from a real delivery or client problem.
EVIDENCE OVER OPINION
Work advances on validated learning against criteria set in advance, not on seniority or hype.
OUR OWN CLOCK
Long-horizon questions are judged on the learning and the options they create, not this quarter’s revenue.
DESIGNED FOR HANDOFF
Where a result will land is agreed before the work starts. Nothing is researched with nowhere to go.
PUBLISHED AND SHARED
Methods, benchmarks, and negative results are written down and shared with partners and clients, not buried.
WE KILL THINGS
A clear stop is a good outcome. It frees capital and tells you something true about the technology.
STEP 1
Prove readiness
Validated demand, a working prototype, and a credible unit economics case.
STEP 2
Name the home
A delivery team, a product, or your own engineering group commits to receive it.
STEP 3
Hand over properly
Code, documentation, people, and IP move together, never a concept with no resources.
STEP 4
Track what happened
Adoption and outcomes followed for two to four quarters to confirm the result took hold.
01
Framing workshop
A short engagement to turn an ambition into a testable question, with the criteria that would make it a yes.
02
Prototype sprint
We build the smallest thing that can fail, run it against your data and constraints, and report what held.
01
Co-funded research bet
A shared programme on a problem neither side can answer alone, with agreed IP terms and a named transfer home.
01
Academic collaboration
Joint studies, student projects, and placements: enterprise problems and data access in exchange for rigour.
The unit runs as a small core with fixed-term tours of duty. Engineers, designers, and data scientists inside dbiz can join a bet, do the work, and take the capability back to their team.
Research & tech leads
Deep expertise in a target domain; drive discovery and technical feasibility.
Venture & product leads
Own a bet end to end: the problem, the case, and the path to transfer.
Engineers & prototypers
Build fast, disposable prototypes that test the riskiest assumption first.
Designers & data scientists
Shape how people work with agents; mine signals and validate what the data supports.
Tell us what you have tried and where it stopped. If it is a question worth answering, we will tell you how we would test it.
Bring a problem your roadmap keeps deferring.
Propose a joint study, placement, or student project.
Put your name forward for the next tour of duty.