Enterprise AI.Built to keep improving.
We build AI into your operations, alongside your team. From deployment to ongoing improvement, a data flywheel connects business feedback, evaluation and model post-training.
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Human Thinks.Agents Execute.
Direction. Action. Continuous improvement.
People set the direction and make the important calls. Agents carry the work forward, with business feedback guiding each improvement.
- Define the work together
- Work with your people to choose a valuable workflow, understand the data and agree what success means.
- Build it into the business
- Connect systems and agents, validate the pilot and equip your team to operate what we build.
- Keep improving together
- Review business performance and use expert feedback to improve workflows, knowledge and models within the agreed scope.
AI, working insideyour business.
We work with your business and technical teams to connect data, systems and agents through MandoForge. From life sciences to commerce, we start with one important workflow and build from there.
Connect research evidence, study records and expert review.
OntologyYour world. Understood.
Research, products, orders and business rules span many systems. We use Ontology to connect objects and relationships with their sources and permissions, giving people and agents a shared business context.
Publications · Studies · Protocols · Observations · Evidence · Reviews
A data flywheel.Built from the work itself.
Capture outcomes, expert corrections and exceptions as traceable examples and evaluation evidence. Each improvement starts with a specific problem and a baseline, then returns to the business to be tested.
Observe the work
Connect tasks and outputs to real outcomes.
Learn from experts
Keep correction reasons and curate useful examples.
Evaluate and improve
Identify changes to workflows, knowledge or models.
Validate the update
Compare versions, then track results in operation.
- Expert feedback
- Researchers correct terminology, evidence links and missing context.
- Improvement focus
- Evaluate extraction and source-grounded summaries against expert-reviewed examples.
Model post-training.Shaped by your expertise.
When evaluations reveal a model capability gap, we use reviewed business examples and expert preferences for post-training. We compare quality, latency and operating cost before bringing updates into the workflow.
Supervised fine-tuning · Preference optimization · Task evaluation
Part of ongoing improvement, with training methods chosen for the business need.
Extract study information · Summarize with sources · Classify research evidence
Start with work worth improving.
Together, we map the workflow, available data and acceptance goals for a first pilot. Each stage has a clear scope, from building and handover to ongoing improvement.