Custom AI Agents for HR Development Services

An AI hiring agent is autonomous software that runs multi-step recruiting tasks like sourcing, screening, scheduling, and interviewing with reasoning over each decision, not a scripted flow. An ATS is your system of record for applications; a recruiting chatbot answers single-turn questions. An AI hiring agent sits on top of both, making decisions and taking actions across tools.

Think of it as the layer that pulls candidate data from your ATS, checks eligibility, schedules the interview, scores the response, and syncs the outcome back. No manual handoff. AI agents in recruitment replace the gaps between your existing tools, not the tools themselves.

Pick the framework that matches the engineering discipline you already have. LangGraph gives deterministic state-machine control for complex multi-step funnels. CrewAI fits team-based agent workflows with clear role hierarchies. AutoGen is strong for conversational multi-agent collaboration. PydanticAI enforces typed schemas end-to-end and plays well with Python backends.

For most production hiring funnels, we recommend LangGraph or CrewAI as the orchestration layer, with PydanticAI on tool contracts and AutoGen for specific conversational stages. We pick per engagement, not by vendor loyalty.

We build bi-directional sync with Workday, Greenhouse, Lever, iCIMS, SAP SuccessFactors, Bullhorn, UKG, SmartRecruiters, BambooHR, and Oracle HCM via their REST or GraphQL APIs, with Model Context Protocol (MCP) tool wrappers for agent-side access. Candidate status, notes, AI-generated insights, and bias-audit data all flow both ways. Your ATS stays the system of record; the agent becomes a decision layer on top.

For ATSs without modern APIs, we build middleware, including queue, cache, transformation, to bridge the gap without blocking your recruiters. Legacy fields stay intact; new AI-generated fields land cleanly alongside them.

We build NYC Local Law 144 compliance into the architecture, not as a retrofit. You get bias-audit artifacts like selection-rate analysis by protected class, four-fifths rule reporting per EEOC guidance, plus candidate-notice flows in the apply stage and an annual re-audit pipeline.

The agent logs every model-driven decision with feature attribution, so an independent auditor can verify without sampling. For EU deployments, we add EU AI Act conformity documentation. For regulated US industries, we align to ISO/IEC 42001:2023 controls.

A typical engagement runs 2-4 weeks for discovery and proof of concept, then 8-16 weeks to a production system, depending on integration scope and compliance surface. A single-agent resume screener integrated with one ATS lands on the short end. A multi-agent funnel covering sourcing through onboarding with bias-audit reporting lands on the long end.

Per industry averages, indicative ranges are: $60,000-$120,000 for a focused single-stage agent; $180,000-$400,000 for a multi-agent funnel with ATS and HRIS integrations; and $400,000+ for enterprise deployments with on-prem, multi-region, or air-gapped requirements. You get a firm estimate in the detailed proposal.

Yes. We deploy on AWS, Azure, or GCP by default, but hybrid, on-prem, or fully air-gapped deployments are available for healthcare, defense, financial services, and regulated-industry buyers. For air-gapped, we use self-hosted open models (Llama, Mistral, Qwen via Ollama or vLLM), self-hosted vector stores (Qdrant, Milvus, pgvector), and a mirrored toolchain so the agent never reaches the public internet.

Data residency, BYOM (bring-your-own-model), and customer-managed encryption keys are all supported. For EU/US split deployments, we run per-region routing so EU candidate data never crosses to US infrastructure.

Routing depends on accuracy-vs-cost tolerance, but a common pattern is Gemini Flash or Haiku for sourcing and parsing (high volume, low complexity), GPT or Sonnet for screening and ranking (mid-complexity reasoning), and Claude Opus or the GPT frontier tier for interview scoring (nuanced language, rubric alignment, explainability). We build routing and automatic fallback so a single provider outage doesn't take your pipeline down.

Self-hosted open models (Llama, Mistral, Qwen) replace any of these when data residency requires it. You get AI recruiting automation without lock-in to a single model provider.

Mobile-first flows are the default for frontline roles in retail, hospitality, healthcare, and logistics. We build autonomous hiring flows on SMS, WhatsApp Business, and career-site chat via Twilio, Vonage, or MessageBird, keeping each application under 2 minutes on a phone screen.

The agent handles multilingual detection, response, and mid-conversation switching across 70+ languages, using the LLM provider's native-language support and ElevenLabs for voice. High-volume clients typically see application completion rates rise from 30-40% on traditional web forms to 80%+ on conversational mobile flows.

Your agent auto-detects the candidate's language and responds in kind across 70+ languages, switching mid-conversation without manual configuration. Regional compliance is mapped per jurisdiction: NYC Local Law 144 for New York, EU AI Act for European deployments, GDPR and UK GDPR for EU/UK candidate data, HIPAA when the agent touches PHI in healthcare staffing, and state-level US regulations (Illinois AI Video Interview Act, Maryland HB 1202, Colorado AI Act) where applicable.

We build per-region data-residency routing so EU candidate data stays on EU infrastructure and US data stays in the US. Consent banners, retention rules, and DSAR handling are configurable per jurisdiction.

We run a discovery phase to map your legacy data model, then build an ETL pipeline that migrates candidate records, application history, notes, and rejection reasons into your target ATS with integrity checks. The AI hiring agent can operate on both systems in parallel during cutover by reading from the legacy ATS and writing to the new one, so recruiters never lose context.