Generative AI strategy
Where a model changes a workflow, what should be left alone, and the sequence of work.
AI & generative AI
Turn generative AI into operational capability.
We help organisations across Australia and Asia-Pacific design, build and run generative AI systems. That covers strategy, Claude and other large language models, enterprise agents, retrieval, proof-of-concepts and the controls required once a system has real users.
Lifecycle
Discover through to optimise.
Coplanar AI can stay with a system for the whole path, or join at the point where a prototype needs architecture, security and a production plan.
01
Discover
Identify the decision, the users and the constraint that will still be true in production.
02
Prototype
Build a narrow version against a real workflow, not a generic chat window.
03
Validate
Test quality, failure modes, data handling and what the system will cost to run.
04
Build
Implement the application, the integrations and the interface people will use.
05
Secure
Access, retention, guardrails and an audit trail that matches the risk.
06
Deploy
Release into the cloud environment the organisation already operates, or one we design.
07
Operate
Monitoring, quality signals, incidents and a named owner.
08
Optimise
Model choice, caching, prompts and the workflow around the system.
Services
What an AI engagement can include.
Enterprise AI agents
Agents bound to a process, a set of tools and a point of human approval.
Claude & LLM solutions
Implementation on Anthropic Claude and other large language models, chosen for the task.
Retrieval-augmented generation
Answers grounded in documents, records and policies the organisation is allowed to use.
Enterprise knowledge assistants
Internal assistants that search approved knowledge and show where an answer came from.
AI workflow automation
Models placed inside an operational workflow, with the existing systems still in the path.
AI application development
Interfaces, APIs and services around model capability, built to be maintained.
LLM integration
Connecting a model to identity, applications and the business systems around them.
Model evaluation
Quality, failure modes and fitness for the workflow, before users depend on it.
AI guardrails
Boundaries on what a system can say, call and write back.
AI security & governance
Access, data handling, retention and an owner for the system once it is live.
AI proof-of-concepts
A working build against success criteria, scoped to support a decision.
Production AI deployment
The path from a prototype to a system people rely on during a working day.
AI observability
Logs, traces and quality signals after release, not only uptime.
AI cost optimisation
Token use, model choice, caching and architecture so the system stays affordable.
Agents
AI agents built for real business workflows.
We design agents around business processes, systems and governance — not demos. Coplanar AI integrates models with enterprise data, APIs, cloud services and human approval workflows.
An agent engagement starts with the process: what it may read, what it may call, when it must stop, and who is accountable for the outcome. The model is selected after that, including Claude where it fits.
Customer service agents
Grounded in policy, product and order data, with a handoff.
Internal knowledge agents
Search over approved material, with sources attached.
Research agents
Collection and comparison, returned for a person to judge.
Document processing agents
Extract, classify and route. Exceptions stay with a reviewer.
Operations agents
Triage across the tools an operations team already uses.
Developer productivity agents
Connected to repositories, tickets and the team's review path.
Compliance assistants
Drafts and checks against a controlled rule set.
Sales assistants
Preparation from approved product and account information.
Data analysis agents
Questions over a defined dataset, not an open warehouse.
Workflow orchestration agents
API calls inside a process that can pause for approval.
Knowledge and control
Data, evaluation and the boundary around the model.
Retrieval and assistants only help if the corpus, the permissions and the failure cases are explicit. The same is true of cost. A system that cannot be measured will not stay in production.
Retrieval and knowledge
Document pipelines, access control, citations and a clear line between approved sources and everything else. This is the usual base for an enterprise knowledge assistant.
Evaluation and guardrails
Test sets drawn from the real workflow, checks on tool use, and filters where the organisation already has a policy. Evaluation is how a proof of concept earns a production plan.
Security, observability, cost
Identity, logging, retention, traces and a view of spend. AI security here means the controls around the system. We do not claim a certification we have not been given.