The vendor that ran their cloud was shutting it down.
The date was fixed, and it wasn't theirs.

Where source files weren't available, our engineers mapped how the existing network actually behaved before rebuilding its connections and controls. The foundation work covered the cloud accounts, network, deployment pipelines and Kubernetes operations:

• AWS Landing Zone Accelerator replaced the proprietary multi-account setup, with a hub-and-spoke network architecture and infrastructure-as-code templates.
• AWS CodePipeline and CodeBuild replaced vendor-managed releases across about 20 tenant application stacks and multiple environments, with version control, monitoring and rollback logic.
• Reusable AWS CDK constructs brought Kubernetes scaling, upgrades and environment configuration under the firm's own control.
• Validation covered connectivity, security policy, pipeline behavior, failover and rollback scenarios, and runbooks and hands-on handover gave the firm's operations team what it needed to run the environment.

The cloud foundation and the deployment tooling now sit with the firm's own team, and the December 2025 shutdown passed without disruption, as the firm reports. Because the deadline was fixed, the work that removed the risk went first and everything else waited for it.

A three-to-four-week discovery assessment, fully funded by AWS, ranked use cases by business value and readiness, reviewed the candidate data and its privacy requirements, and benchmarked Bedrock models on recruiting tasks such as drafting job descriptions, summarizing candidates and writing outreach.

A six-to-eight-week proof of value then connected three capabilities to the existing platform, on the firm's real data:

• Candidate profiles are represented semantically and indexed in Amazon OpenSearch, which lets a recruiter search by meaning. A job description comes back as a ranked list, and each match carries a written explanation of fit, so the recruiter has a reason to look at it.
• Transcripts, emails, and documents all feed into an enrichment pipeline that keeps profiles current on their own. Bedrock extracts structured fields such as role, skills, and experience, and an integration writes them straight back into the CRM.
• During an interview itself, Amazon Transcribe Streaming sends the live transcript to Bedrock, and the application shows suggested follow-up questions while the conversation is still happening.

The deployment design runs application containers on Amazon EKS in private subnets across two Availability Zones, with OpenSearch and Amazon RDS for search and operational data, and Secrets Manager, the delivery pipeline and AWS monitoring services alongside. Because it's built on the same platform architecture as production, what was proved can move straight into production.

Search a database and you get whatever matches the keywords, which is rarely what the hiring manager meant; a good recruiter asks questions first. The agentic platform brings both together in one connected workflow, so a hiring manager can clarify the requirement and refine the results in the same conversation.

• An AI agent asks the clarifying questions, keeps the conversation's context inside the client boundary, and turns the discussion into structured search parameters.
• Candidate discovery calls the existing database and recommendation services, and returns a reasoned shortlist to refine.
• For senior searches, research AI agents look at relevant public information. The output keeps external research separate from internal CRM information, so the source of every statement stays visible.
• Through the platform's integrations, the workflow can generate job descriptions, draft outreach, and start candidate-contact processes.

Amazon Bedrock supplies the models and the AI-agent layer, and the production design uses Bedrock AgentCore for hosting and tool integration. The AI sits alongside the firm's existing recruiter workflows rather than replacing them.

Why: with internal and external information kept apart, a recruiter can always see which facts came from the firm's own records and which came from the web.

Contractors had kept the ranking system running, without one owner, runbooks or alerts. We took responsibility for it through an ongoing engineering team with a shared backlog and weekly releases, covering India and U.S. working hours:

• Health checks and alerts cover pipeline failures, data quality, model performance and connectivity, which means an engineer sees a problem well before a recruiter notices anything wrong.
• Every new model version is evaluated against the current production model, with regression tests, staged rollout and automatic rollback in the release path.
• Dashboards bring pipeline reliability and model metrics together with processing volume and cost, so leadership can see the whole engine for the first time.
• Interview outcomes feed back into later ranking improvements, along with LLM reasoning for profile matching and scoring for cultural and organizational fit.

Finance, recruiting, spreadsheet and search-activity data came together in one analytics model, using Amazon S3 and AWS transformation services, and the data no longer leaves AWS to be reported on. Amazon QuickSight supplies executive, finance and operations dashboards, with email subscriptions for key metrics. QuickSight Q is configured with the firm's own vocabulary and example questions, so leadership can ask about margins, revenue and pipeline in plain language, without commissioning a report for each answer.