Agentic AI, from the first use case to production.
A small senior team does the work. We design agents with your team, connect them to your systems, test them, and limit their cost. Then we hand over the work, so your team can run it.
Readiness and use-case discovery
Find the tasks where an agent helps, and the tasks where it does not.
Talk to us about thisWhat you get
- A ranked list of use cases with the value, the required access, the risk, and the approval points of each.
- One recommended first build with a scope and acceptance tests.
How it runs
We interview the team that does the work and review your systems and data. Then we write a report and review it with you.
Agentic workflow design and delivery
A working agent in your environment, with a person in charge of each consequential action.
Talk to us about thisWhat you get
- An agent with tools that call your systems, approval steps, and an audit trail.
- The code, the tests, and the runbooks in your own repositories.
How it runs
We agree a written scope with acceptance tests. We build in short cycles with a weekly demo. Then we release the agent to a small group first.
We build this in our own product
The VikaHire assistant proposes changes, and a person confirms each change before it runs. After an AI interview, a candidate moves forward only when a person records the decision.
Evaluation, guardrails, and governance
Find agent failures before release, and keep a record of each approval.
Talk to us about thisWhat you get
- A test set from your real tasks, with a pass bar.
- Quality gates that run before each release.
- Input handling that treats documents and user text as data, not as instructions.
- A record of each approval, with data handling that follows your policies.
How it runs
We collect real cases with your team and write the tests. Then we connect the gate to your release process.
We build this in our own product
VikaHire gives résumés and candidate answers to the model as untrusted data. Each AI interview score cites evidence in the transcript. We test candidate matching against a synthetic evaluation set, with a fairness audit.
Cost control and operations
Know what each AI feature costs, and stop it before it costs too much.
Talk to us about thisWhat you get
- A usage record for each AI call: the feature, the provider, the model, and the amount.
- Limits with a warning and a hard stop, and budget caps at each AI provider.
- Monitoring for errors and cost.
How it runs
We measure your current use and set limits with you. Then we give your team the dashboards and the alerts.
We build this in our own product
VikaHire records each AI call by feature, provider, model, and amount, without the prompt or the answer. A limit warns at 80% and stops new AI interviews at 100%.
What you get
- Hands-on sessions built around real tasks from your backlog.
- Written material that stays with your team.
How it runs
Engineers attend a series of working sessions. Managers attend a shorter session on where agents fit and where a person must decide.
Every engagement runs the same way.
You know what you get and how we measure it before the work starts.
A first call
We learn the work, the systems, and the limits.
A written scope
The deliverables, the acceptance tests, the timeline, and the price.
Short cycles
We review working output with you each week.
Handover
The code, the tests, and the runbooks go to your repositories. Your team can run the work without us.
What we do not promise
We do not resell a platform. We do not promise that an agent cannot fail. We build agents that a person can supervise, test, and stop.
Tell us about one task.
Tell us what the task is, who does it today, and which systems it touches. We will tell you whether an agent fits.