AI agents that plan and run projects across teams.
Set the outcome. InferenceHQ gives the project an accountable AI lead and specialist agents that build the plan, do the follow-through, act across your tools with approval, and verify the result. You keep the decisions.
Start with one live project. Your team keeps its current tools.
Get Acme live by August 15
Security is now on the critical path. I updated the rollout plan, moved training behind review, and assigned the missing checks. The customer request is ready for Maya.
Checking 27 customer fields against the migration requirements.
Reordered validation, security review, training, and go-live.
Drafted the owner request and revised launch update for Acme.
The AI lead keeps the whole project moving.
It owns the plan, delegates work to specialist agents and people, keeps every output current, and brings you the decisions that still need human judgment.
The AI lead turns the outcome into milestones, owners, dependencies, deliverables, and a definition of done, then updates them as the project changes.
Specialist agents investigate unknowns, revise documents, prepare communication, update systems, and chase bounded next steps.
You see the choice, evidence, consequences, and prepared action. Consequential work waits for your approval.
Make one decision. The whole project moves with it.
The AI lead carries your direction to every affected specialist, updates the plan and outputs, and returns with the next decision. You do not brief each agent again.
“Security review has to finish before training. The customer will not accept a provisional launch.”
Your AI team carries the project through four steps.
The same team stays accountable from the first sentence through planning, execution, and proof.
Describe the result
InferenceHQ gathers the authorized company context and gives the project an accountable AI lead.
Build the path
The lead and specialists create the milestones, deliverables, owners, dependencies, and definition of done.
Do the project work
Agents investigate, draft, follow up, update systems, and prepare consequential actions for approval.
Check the result
The team tests completion against the agreed criteria. A failed check automatically reopens the work.
Carry more projects without carrying every handoff.
Your AI teams handle planning, project work, coordination, and follow-through. You spend your time on judgment, tradeoffs, and relationships.
Let the agents work without giving up control.
Every agent has a named job, scoped company access, and explicit authority. Customer promises, external messages, spending, and other consequential actions wait for a person.
Each project sees only the authorized sources, people, decisions, and connected work required for its outcome.
An approval covers only the listed actions. External communication remains separately approved.
Competing recommendations keep their evidence, consequences, and objections until a person decides.
Every completed work unit has evidence against its definition of done or a visible unresolved judgment.
Built for the person who has to deliver.
The AI team changes with the job. So do its plan, specialists, connected systems, outputs, and definition of done.
Ship a cross-functional launch.
Product, engineering, design, GTM, legal, and customer context work from the same current plan and deliverables.
Before you start a project.
How is this different from a project tracker?
A tracker records tasks. InferenceHQ gives the project an AI team that builds and maintains the plan, produces the work, coordinates follow-through, takes approved action, and checks the result.
How is this different from AI chat or enterprise search?
Chat responds when prompted, and search finds information. InferenceHQ gives a persistent AI team responsibility for an outcome, its plan, live outputs, next actions, and proof of completion.
Do I need the whole company to adopt it?
No. Start with one person accountable for one active cross-functional outcome and the teammates who already participate. The project can connect to the tools the team uses today.
What can the agents actually do?
They can research, analyze files, create and revise project artifacts, investigate changes, prepare decisions, assign bounded follow-up, update connected systems, send separately approved communication, and verify completion criteria.
What kind of project should we start with?
Choose a consequential project with a clear person responsible, several collaborators, real artifacts, changing information, and a result you can verify. A launch, implementation, enterprise pilot, migration, or company-critical initiative works well.
Give one important project an AI team.
In 20 minutes, we’ll choose the project, define the outcome and approval boundaries, and show you the first work its agents can carry.