Bring governed AI into the everyday work of the public sector.
AI does the sorting, searching and chasing. Your teams keep the decisions. cognipeer helps government and public sector organisations build AI workflows for operations, knowledge, finance and training teams, with access rules, approval points, guardrails and tracing.
Public Sector AI Peer
Inboxes and request channels
Emails
Requests
Categories
Teams
Operations team asks
Sort today’s incoming requests and send each one to the team that handles it.
Workflow steps
Put AI inside your organisation’s workflows, not beside them.
Public sector work runs on inboxes, past records and documents, budgets and meeting notes. cognipeer connects AI to the sources each workflow needs, so it can sort, search, summarise or follow up, then pass the result to the person who owns the next step.
Record search
Authorised viewRelevant records
Sources found for the question
Summarised findings
Missing information
Responsible teams
Current step
Findings summarised
Follow-up
Stays with the responsible team
Public Sector AI Peer
Past records and documents
What do our past records say about this process, and what is missing?
I have summarised what the records and documents say and marked the information that is missing. How to follow it up stays with your team.
AI works better when requests, records and owners are connected.
Bring together the sources each public sector workflow depends on, so AI can do the groundwork and pass it to the person who owns the next step.
Inboxes & request channels
Past records & documents
Chosen web sources
Budget & actual figures
Meeting notes & transcripts
Task management
Attendance & assignments
Learning platform & feedback
Reduce the manual sorting, searching and chasing in everyday public sector work.
In many public sector workflows, someone has to read, sort, search, chase or compile before the real work can begin, and the result can vary from one person to the next. That groundwork is where AI can help first.
Requests wait to be sorted
Each email or request has to be opened and assessed before anyone can decide who should handle it, so requests can wait before they reach the right team.
Knowledge is scattered, and gaps are easy to miss
Answers sit across past records and documents, and missing information or fields still to be completed are easy to overlook.
Follow-up depends on someone chasing
Month-end explanations have to be requested and chased by hand, and actions agreed in meetings are not always linked to an owner or a deadline.
Signals depend on someone noticing
Key topics are followed by checking sources one by one, and signs that a training participant is struggling are often noticed late.
Build public sector AI around the work your teams already do.
Connect the sources each workflow needs, then shape the AI Peer around the output your teams review and the decisions they keep.
Discuss your public sector workflowRoute requests and decisions to their owners
Categorise, summarise and route incoming requests to the right workflow or team, and turn meeting notes into one summary of decisions, owners and deadlines.
Find what your organisation already knows
Answer questions from past records and documents, summarise the findings and show what is missing, so the responsible team can follow it up.
Collect input without manual chasing
Ask budget owners for their month-end explanations, follow up late replies and consolidate the answers into one summary for management. Budget owners still write the explanations.
Point teams to what needs attention
Prepare a morning bulletin of new content on chosen topics, sorted by importance, and flag training participants showing signs of risk, with suggested interventions for trainers and coordinators to consider.
Public sector AI use cases from real customer projects.
Every use case below comes from an anonymised customer project and links to the full case study. Start with the one closest to your own work.
Incoming requests and meeting actions reach the people who own them.
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Incoming request classification
Incoming emails and operational requests categorised, summarised and routed to the right workflow or team.
Read the case study -
Meeting decisions and owners
Decisions, open actions, owners and deadlines drawn from meeting notes and transcripts, then shared with the team as one summary.
Read the case study
Answers from past records and a regular view of the topics teams follow.
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Knowledge search across records
Questions answered from records and documents, with findings summarised and missing information made visible.
Read the case study -
Morning topic bulletin
A morning scan of chosen sources and topics, with new content sorted by importance, summarised and prepared as an email bulletin.
Read the case study
Month-end explanations collected from budget owners, and training participants who may need support identified early.
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Budget variance explanations
Budget variances identified at month-end, explanations requested from budget owners and consolidated into one summary for management.
Read the case study -
Training participant follow-up
Participants at risk of dropping out identified early, with intervention suggestions and draft messages for trainers and coordinators.
Read the case study
A project for a company that sells to public sector institutions, built for its bid and sales teams.
AI did the groundwork. Public sector teams kept the decisions.
Anonymised customer projects built with cognipeer Studio. In each one, AI handled the sorting, searching or follow-up, and the institution’s own teams decided how to act.
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Public sector
How a Public Sector Institution Used AI to Classify Incoming Requests
What AI didCategorised and summarised incoming emails and operational requests, then routed each one to the right workflow or team.
What stayed with peopleRead the case studyHandling each request. The receiving team remained responsible for it, starting from a clearer picture of what it was about.
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Public sector
AI Knowledge Search for a Public Sector Institution
What AI didAnswered questions from past records and documents, summarised the findings and made missing information visible alongside the answer.
What stayed with peopleRead the case studyDeciding how to act on the findings, and following up and completing the records, owned by the relevant teams.
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Public sector
AI-Assisted Budget Variance Explanations at a Public Sector Institution
What AI didAt month-end, identified budget variances, asked budget owners for explanations, followed up late replies automatically and consolidated the answers into one summary for management.
What stayed with peopleRead the case studyThe explanations themselves, written by budget owners. The finance team worked from the summary when preparing material for management.
Our case studies describe real customer projects and are anonymised.
Ask once. Bring decisions and owners together.
A public sector AI Peer can read meeting notes and transcripts, pick out decisions, open actions, owners and deadlines, and share one summary with the team. Your team checks any task draft before it is added.
Public Sector Peer
AI agent · Meeting records and owners
What did we agree in this morning’s project meeting, and who owns each action?
Pulled from the notes and transcript
Here is one summary of the decisions, open actions, owners and deadlines from the notes and transcript. Share it with the project team?
Yes, and prepare a draft for our task list.
Summary shared. The task draft is ready for you to check. Nothing is added to your task management system until you confirm.
Illustrative example based on an anonymised customer project. No real meeting, people or data are shown.
Connect AI to your organisation’s systems, inside your controls.
Build public sector workflows in Studio and manage models, guardrails, tracing and usage in Console, the self-hosted AI gateway and control plane. Decide where cognipeer runs, what each Peer can access, where people approve and what is recorded.
Discuss your deployment and controlsYour knowledge and systems
Ground Peers in documents, websites, APIs and databases, and connect tools that update the systems your teams use.
Deployment options
cognipeer supports cloud, private and self-hosted environments, according to your architecture requirements.
See enterprise deploymentGuardrails and personal data
Console guardrails can detect personal data such as email addresses and phone numbers, then block, mask, warn or flag it before a model call and before an answer is returned.
Tracing and audit
Console traces agent executions, tool calls and model requests, and produces trace events, audit logs and usage exports. It can be configured to support your audit and policy requirements.
Access and approvals
Set data access, tool permissions and approval points for each use case in Studio. Scope API keys and quotas by project in Console. Console EE adds directory authentication, JIT provisioning and group-to-role mapping.
Model choice
Choose the model for each Peer or project. Console offers an OpenAI-compatible API with routing and fallback across providers.
cognipeer supports the groundwork. Decisions on requests, budgets, training and follow-up stay with your organisation’s accountable teams.
Start with one public sector workflow, then expand.
Begin with a workflow that has a clear owner and a lot of manual sorting, searching or chasing, then expand into related workflows as your teams gain confidence.
01
Choose a workflow with a clear owner
For example request classification, knowledge search or month-end explanations. Agree the sources it uses and who decides.
02
Define the Peer
Set its sources, outputs, limits and review steps, and test it with evaluation suites before rollout.
03
Connect approved actions
Add the handover, such as routing to the responsible team, requests to budget owners or task drafts for review, then expand to the next workflow.
Questions public sector teams ask before they start.
Short answers on deployment, decisions, tracing and models. Bring any other questions to a demo.
Can cognipeer run inside our own infrastructure?
Yes. cognipeer supports cloud, private and self-hosted environments, according to your architecture requirements. Bring your hosting and security requirements to a demo, and we will go through the options with you.
Does the AI make decisions on its own?
You decide where people step in. Studio workflows can include user approval steps, and routine work can continue while sensitive or gated actions pause for a human decision. Some routine steps in our public sector case studies ran automatically: follow-ups went to budget owners who had not yet replied, and a bulletin was prepared each morning, which could also be shared automatically with the relevant teams if the institution wished. The decisions stayed with people: the receiving teams handled each request, budget owners wrote their own explanations, and teams decided how to act on what the AI found.
Can we trace what the AI did?
Yes. Console traces agent executions, tool calls and model requests, and provides trace events, audit logs and usage exports. It can be configured to support your audit and policy requirements.
Which models can we use?
Choose the model for each Peer or project. Console provides an OpenAI-compatible API with routing and fallback across providers.
How do we start?
Start with one workflow and a clear owner. We help with use case design, knowledge and model design, integration and rollout. Book a demo and bring the workflow you want to start with. For a longer walkthrough, read our guide to AI agents in the public sector.
Put governed AI to work across your organisation.
Build public sector AI Peers that do the groundwork, keep decisions with your people and run within your controls. Book a demo and bring one workflow you want to start with.