Savo RecruitAI
Screens resumes, runs structured first-round chats and ranks candidates against your scorecard, bias audited.
Summarise this application against our backend role scorecard.
API experience · Testing · Database work
Use the relevant information. Keep missing context visible.
API and database experience are described. Evidence of automated testing is incomplete - flag it as a question for the interview, not a rejection.
A recruiter checks the evidence and makes the decision.
A focused role, connected to your work.
RecruitAI reads every resume against your scorecard, runs structured first-round conversations, and delivers a ranked shortlist with evidence for every decision. Bias audits run on each cohort so your process stays defensible and fair.
A useful scope, not unlimited autonomy.
These capabilities define the starting conversation. Sources, integrations and permissions are agreed for your workflow.
- Resume screening against a custom scorecard
- Structured async first-round interviews
- Ranked shortlists with per-candidate evidence
- Bias audit report for every hiring cohort
Useful intelligence needs clear boundaries.
The model is only one part of the system. Explore the information, permissions and review paths around it.
Relevant information
Select useful sources, respect access and make the supporting context available. Missing or stale information should be visible, not silently filled in.
Bounded actions
Specify what the system may read, prepare or change. Sensitive actions need the right permission and, where appropriate, a person's approval.
Evaluation & fallback
Test routine questions, incomplete inputs and difficult cases. Define when to ask, stop, retry or hand off rather than assuming every answer is correct.
An operating plan
Agree who reviews usage, handles incidents and maintains prompts, data or integrations. Monitoring, cost controls and review records depend on the deployment.
AI is not always the answer.
Clear rules may be simpler. Thin information may need attention first. For sensitive decisions, AI can prepare context while a qualified person decides.
Explore AI strategyA clear path from idea to operation.
Start with a focused scope. Make the information, evaluation and operating responsibilities part of the work.
Scope the job
Define the workflow, the information it needs and the actions it is allowed to take.
Connect the knowledge
Prepare retrieval, integrations and access around your actual systems.
Test the boundaries
Evaluate useful answers, failure cases, approval steps and human handoff.
Launch with supervision
Start with agreed limits, review real usage and adjust the system with evidence.
Illustrative delivery stages. Scope and timing are agreed for your project.
Explore another role.
Different tasks need different information, tools and review paths.
A few things worth asking.
Practical answers about scope, information and what happens after the first build.
Timing depends on the workflow, the state of your information and the integrations involved. We define the first useful scope and the evaluation work before agreeing a delivery plan.
That depends on the agreed architecture and model providers. We review hosting, access, retention and provider terms with you. A private deployment may be appropriate, but data-location and confidentiality requirements need to be confirmed in the project scope.
We define permitted reads and actions, approval steps, escalation rules and review records. Sensitive actions can require a person to approve them. These boundaries are tested and revised with the team that operates the agent.
They can be connected within a workflow, with a defined responsibility and permission boundary for each part. For a straightforward task, one agent or a rule-based automation may be a better starting point.
No. When the rules are clear and the inputs are consistent, an integration or rule-based automation may be simpler. An agent is worth considering when a task needs language understanding, retrieval or tool selection, with a suitable review path.
The workflow should have an agreed fallback: ask for more information, stop an action, use a simpler route or hand over to a person. AI can still produce incorrect answers, so testing and ongoing review are part of the work.
AI project questions
Start with the work you want to change.
Tell us the task, the systems involved and where your team needs help. We’ll work out whether AI, automation or a simpler approach fits.