Transform Your Cloud Costs with AI-Powered FinOps

B2B SaaS (Retail Technology) · 300-500 employees · 12 weeks

Challenge

After acquiring a smaller competitor, this retail technology company was running two separate cloud estates — an AWS-heavy environment from the parent company and an Azure-heavy setup from the acquisition. Finance received two separate bills with no shared taxonomy. Approximately 40% of resources carried no meaningful ownership tag, and reserved instance coverage on AWS sat at 19%, leaving most compute on on-demand pricing. The CTO needed a consolidated governance baseline before the next board review.

Approach

We connected to both environments via read-only API access and completed a baseline cost analysis in 5 days. The first week surfaced 52 zombie resources — idle VMs, unattached disks, and forgotten load balancers from the migration project — representing $24K/month in zero-risk immediate savings. We established a unified tagging standard enforced by AWS Config Rules and Azure Policy. In week 3, we modeled a reserved capacity strategy based on 90-day utilization data, identifying $38K/month in additional savings from a staged reservation purchase across both environments.

Outcome

Monthly cloud spend declined from $175K to $101K within one quarter — a $888K annualized reduction, with the full $2.1M figure reflecting the compounding effect of reservation commitments over their term. Reserved instance coverage reached 74% from 19%. The tagging enforcement policy eliminated unowned resource growth. The weekly FinOps standup cadence the team adopted during the engagement became a permanent ritual, and six months post-engagement the team independently drove a further 12% reduction.

B2B SaaS (Legal Services Automation) · 75-150 employees · 6 weeks

Challenge

This legal services automation company's support and customer success teams spent an estimated 35-40% of their time searching a 500K+ article knowledge base accumulated over 8 years of product releases and client documentation. The existing Elasticsearch keyword search required exact phrase matching, returned multiple irrelevant results for most queries, and averaged 8 seconds per search. Two SaaS AI search products had been evaluated but both required sending documents to external APIs — a non-starter given client confidentiality obligations and Indian data residency requirements under DPDPA.

Approach

We deployed the full pipeline within the client's existing AWS VPC using a self-hosted Qdrant vector database on an EKS cluster. LlamaIndex handled document ingestion with recursive chunking tuned for multi-section legal and compliance documents. The retrieval layer used hybrid search — dense vector cosine similarity combined with BM25 keyword scoring — with a cross-encoder reranker that improved top-3 retrieval precision by 28% in offline evaluation. Generation used Claude Haiku via AWS Bedrock, keeping all inference within the VPC boundary. An internal REST API layer let the existing support tooling integrate without any client-facing changes.

Outcome

Search latency dropped from 8 seconds to under 500ms at P95 — a 94% improvement. Support ticket escalation rate fell 34% in the first 30 days as agents consistently found accurate answers on the first search. The system handles 2,500-3,000 queries per day at 99.7% uptime. Inference cost at $0.0008 per query runs below the previous Elasticsearch licence fee, making the operating economics immediately positive.

Financial Services (Consumer Lending) · 200-400 employees · 6 weeks

Challenge

This consumer lending platform processed personal and financial data for approximately 800K registered users under India's Digital Personal Data Protection Act 2023. A major enterprise client had issued a compliance readiness questionnaire with a 90-day deadline, threatening contract termination if DPDPA compliance could not be demonstrated. An internal audit had already identified 6 critical gaps: no documented data map, no formal consent management, no data retention enforcement, no breach notification procedure, no data principal request workflow, and no appointed Data Protection Officer.

Approach

We ran a 5-day data mapping sprint using AWS Macie and custom discovery scripts to catalog personal data across 14 databases and 3 third-party integrations. A consent management layer was implemented with a custom CIAM module that captured, stored, and versioned consent records in an immutable audit trail. Data retention policies were encoded in AWS DLM for structured data and S3 lifecycle rules for object storage. We drafted a DPDPA-compliant privacy notice and data principal request workflow, integrated breach notification logic into the existing incident management system, and produced the full compliance evidence pack required for the client audit.

Outcome

Full DPDPA compliance documentation was delivered in 6 weeks. The enterprise client renewed their contract. A subsequent third-party audit confirmed compliance across all 6 original gaps. The consent management system now processes 12,000+ consent events per day. No personal data incidents have been recorded since deployment.

Healthcare (Digital Health Platform) · 100-300 employees · 8 weeks

Challenge

This digital health platform stored clinical notes, lab results, and health records for 50K+ patients across multi-tenant AWS infrastructure. The existing security model relied on VPC perimeter controls and IP allowlisting — a flat trust model that granted any authenticated internal service access to any other service in the VPC. A penetration test commissioned by a medical device partner had identified 3 lateral movement paths allowing a compromised microservice to access patient records across tenants. The platform's GCC expansion roadmap added UAE ADHICS and Saudi NDMO compliance requirements to an already complex security surface.

Approach

We implemented zero trust architecture using AWS Verified Access for human operator access and mutual TLS (mTLS) for all service-to-service communication. IAM roles were decomposed from 4 broad service roles into 47 least-privilege roles using AWS IAM Access Analyzer to identify the minimum policy required by each service. Kubernetes network policies in the EKS cluster were rewritten using Cilium to enforce microsegmentation by namespace and label selector. AWS GuardDuty and Security Hub were configured with automated remediation for specific finding types, replacing manual triage for high-frequency, low-risk alerts.

Outcome

All 3 lateral movement paths identified in the penetration test were closed within the first 6 weeks. Zero security incidents in 18 months since deployment (October 2024 — April 2026). The HIPAA-aligned access control model satisfied UAE ADHICS requirements for the GCC launch. Annual security operations cost decreased by 22% as automated remediation replaced manual triage for the majority of GuardDuty findings.