Services
Built with you, not just sold to you.
Kera Operator is our product. Our services practice exists because most valuable AI work needs shaping around a specific business — its data, its systems and its constraints.
Service 01
Custom AI agents for businesses
We take one high-value workflow and turn it into a reliable agent. That means mapping the process with the people who do it, designing the tool surface, building the evaluation set before the agent, and then shipping something your team trusts enough to leave running.
- Workflow discovery and feasibility scoring
- Agent architecture: planning, tools, memory, guardrails
- Evaluation harness with regression suites
- Human-in-the-loop design and escalation paths
- Production monitoring, cost and quality dashboards
Service 02
Web & app development
Product engineering teams that ship. We build the web platforms, mobile apps, internal consoles and dashboards that your AI needs to live inside — designed properly, tested properly, and handed over with documentation you can actually use.
- Product design and design systems
- Next.js, React, TypeScript and Node platforms
- iOS and Android applications
- Cloud infrastructure, CI/CD and observability
- Accessibility, performance and security review
Service 03
Building AI models
When an off-the-shelf model cannot get there, we build one. Our research engineers handle data strategy, architecture, training and the unglamorous evaluation work that separates a demo from a dependable system.
- Data pipeline, labelling strategy and synthetic data
- Architecture selection and training runs
- Multimodal, vision-language and domain models
- Rigorous evaluation, red-teaming and bias review
- Inference optimisation, quantisation and serving
Service 04
Fine-tuning AI models
Adapt a frontier model to your domain, your tone and your rules. Fine-tuning is usually the fastest, cheapest route to a model that behaves like your best specialist rather than a well-read generalist.
- Supervised fine-tuning and instruction tuning
- Preference optimisation (DPO / RLHF)
- LoRA and parameter-efficient adaptation
- Distillation into smaller, cheaper models
- Continuous retraining as your data moves
Engagements
Three ways to work with us.
Every engagement starts with a written scope, a success metric and a date.
2 weeks
Discovery sprint
We assess your workflows, score them for automation feasibility and return a costed roadmap.
4–8 weeks
Production pilot
One workflow taken to production with an evaluation harness and agreed success criteria.
Ongoing
Managed operations
We run, monitor and improve your agent fleet while your team focuses on the business.
Process
How a Kera project runs.
01
Understand
Sit with the people doing the work. Document reality, not the process diagram.
02
Evaluate
Build the test set first, so improvement is measurable from day one.
03
Build
Ship weekly. Every increment runs against the evaluation suite.
04
Harden
Failure modes, guardrails, approvals, security review, load testing.
05
Operate
Monitor quality and cost in production. Retrain and improve on a cadence.
Tell us what you are trying to build.
A short call is usually enough for us to tell you whether it is an agent problem, a model problem, or simply an engineering problem.