The demo is rarely the problem. Data that isn’t ready, permissions that don’t hold, evaluation nobody trusts. That is where enterprise AI stalls. dRSTi exists to solve those problems ahead of the delivery work, so our clients are not the ones discovering them in production.
The unit runs on its own clock and its own funding, so long-horizon questions get answered properly. Everything it produces is built to be handed to a delivery team, or to you.
for organisations that invest consistently in research and innovation versus those that don’t
corporate innovation labs fail to produce real business impact, usually because nothing transfers
core, emerging, and long-term bets funded together so today’s delivery and tomorrow’s options both advance
Every piece of work moves through them.
We track where the technology is actually going and frame the questions worth answering, not the ones easiest to demo.
Prototypes and experiments against real data, real permissions, and real cost envelopes. Evidence decides what continues.
What holds up moves into our platforms and delivery teams, or into your stack, with the people and IP attached.
We grow the people, methods, and partnerships that make the next answer faster to reach than the last one.
Agentic AI, taken seriously. Including the parts that are not the model.
Each area below is a reason enterprise AI fails to reach production, and a funded programme working on it. The horizon says when it pays off: the same 70/20/10 balance the portfolio runs on.
Most agent programmes stall on the data, not the model. We research the modelling, lineage, and retrieval patterns that make enterprise data usable by autonomous systems.
How agents should be decomposed, orchestrated, and supervised when the work spans several enterprise systems and the cost of a wrong action is real.
Identity, permissioning, and data residency when the actor is an agent. Prompt-injection and exfiltration surfaces, tested against real enterprise topologies.
What it takes to certify an agent for production and keep it certified: evaluation harnesses, drift detection, audit trails, and the regulatory reporting that follows.
How work, roles, and accountability change once agents run parts of the process, and which reliability and cost thresholds have to be met before they can.
Six stages · five gates.
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.
Research that helps clients this year sits alongside work that will matter in three. Both are funded, so neither starves the other.
Improvements that reach client engagements and our platforms inside a year.
New products and services with early traction, scaled with lead clients.
Exploratory work on technology that could reshape how enterprises operate.
Illustrative allocation of effort and capital: the mix is rebalanced as work matures and graduates between horizons.
Protected funding and time, but never an ivory tower: every question comes from a real delivery or client problem.
Work advances on validated learning against criteria set in advance, not on seniority or hype.
Long-horizon questions are judged on the learning and the options they create, not this quarter’s revenue.
Where a result will land is agreed before the work starts. Nothing is researched with nowhere to go.
Methods, benchmarks, and negative results are written down and shared with partners and clients, not buried.
A clear stop is a good outcome. It frees capital and tells you something true about the technology.
The hardest part of research is the handoff. We name the receiving team and the success criteria before work begins, so what we prove lands somewhere ready to scale it.
Validated demand, a working prototype, and a credible unit economics case.
A delivery team, a product, or your own engineering group commits to receive it.
Code, documentation, people, and IP move together, never a concept with no resources.
Adoption and outcomes followed for two to four quarters to confirm the result took hold.
A short engagement to turn an ambition into a testable question, with the criteria that would make it a yes.
We build the smallest thing that can fail, run it against your data and constraints, and report what held.
A shared programme on a problem neither side can answer alone, with agreed IP terms and a named transfer home.
Joint studies, student projects, and placements: enterprise problems and data access in exchange for rigour.
Co-fund a research bet on a problem your roadmap keeps deferring: data readiness, agent assurance, autonomy in a regulated process. You get evidence and a transferable result; we get a problem worth solving.
Open to dbiz teams and external researchers.
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.
Deep expertise in a target domain; drive discovery and technical feasibility.
Own a bet end to end: the problem, the case, and the path to transfer.
Build fast, disposable prototypes that test the riskiest assumption first.
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.