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Enterprise AI Platform
AI Intelligence & Models
AI Assistant & Productivity
Agent Platform & Orchestration
Guides4 mins read
Most applications already speak the OpenAI chat format. Serving your own model behind a compatible endpoint means no client changes.
Save inference weights, publish them under a model name, and point your client at the DataObserve endpoint. Streaming, tool calls and usage reporting work as before.
Guides
Fine-tuning or prompting? A practical decision guidePrompting gets you started; fine-tuning gets you consistent. A checklist for choosing the right tool for each task.Most teams reach for prompting first, and they should. It's fast, cheap to change and easy to review. But prompts hit a ceiling when a task needs a consistent format, specialised vocabulary or behaviour that's hard to describe in words.Continue readingGuides
Build an evaluation set your team will actually trustFifty real cases beat five thousand synthetic ones. A step-by-step guide to evals for production models.6 mins readGuides
A red-teaming checklist for enterprise assistantsTwelve probes every assistant should survive before it meets a customer, with examples from regulated industries.6 mins readGuides
Data governance for AI teams, without the slowdownWho can train on what, and how to prove it later. Practical controls that keep projects moving.5 mins readProducts
Turn one-off answers into scheduled reports with CoworkerAsk once, then let Coworker deliver the answer every Monday, shared with the people who need it.3 mins readResearch
The silent failure in long RL runs: trainer–inference mismatchA tiny numerical gap between rollout and training log-probs can bend a week of learning. How we detect it early.8 mins read
Model training
General-purpose models are remarkable, but they don't know your contracts, your claims rules or the shorthand your engineers use. The gap between a demo and a dependable system is almost always domain knowledge. Learning from experience closes that gap. Instead of prompting harder, you let a model practise on your tasks, score the results against what good looks like, and keep the improvements.
6 mins readContinue ReadingModel training
What's new in the model catalog: 174 open-weight modelsNew MoE releases, longer contexts and per-model correctness tests: the autumn update to the catalog.4 mins readResearch
Human preference optimization, explainedRLHF, DPO and what preference data really needs to look like for enterprise assistants.7 mins readProducts
Pinned story packets: how Developer keeps builds traceableEvery coding task carries the exact scope, PRD and TDD versions it was approved against.4 mins readModel training
Collecting multimodal training data with consentFrom egocentric video to contact-centre calls: how we record, label and deliver training-ready datasets.5 mins readProducts
Turning the GPUs you own into a model labRun the same training, inference and Console on your own cluster, with your data and weights in your environment.4 mins readResearch
How much domain data is enough?Early results on scaling curves for domain adaptation, and why the first thousand examples matter most.9 mins readProducts
A tour of the Training ConsoleRuns, checkpoints and deployments in one place, without losing sight of the experiment.3 mins read// Ready to see the DataObserve in action?
// Designed for enterprises
Review security, data handling and deployment requirements with our team.
Keep sensitive information within your organization’s approved data boundaries.
Define who can access connected data and which actions require approval.
Plan deployment around your infrastructure and operational requirements.
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