SaaS and ISV on AWS
SaaS platforms hit the same wall around year five. The architecture cannot absorb new tenants. The analytics layer charges per seat. The AI roadmap needs data the platform was never built to expose. We build multi-tenant foundations on AWS, run customer-funded MAP migrations, and ship production GenAI inside Bedrock with the audit controls enterprise customers actually require.
Real engagements, real numbers. Where Armakuni has already shipped in this vertical.
Tenant isolation, licensing creep, an AI deadline, and a modernization plan that stopped moving. We have shipped through all four.
150+ database tables per tenant, no automated provisioning, no database-per-tenant isolation. Onboarding cost scales linearly with engineering capacity rather than sales.
Snowflake, Sisense, and BI tools built for single-tenant assumptions. Per-seat costs growing with every new user. No exit path the analytics team can build alone.
CTO mandate to ship a 3-5 year AI plan in 12 months. Customers churning over feature complexity. Sales unable to demo without engineering time the team does not have.
Year-long internal Angular upgrade and ColdFusion replacement that has not moved. Engineering time absorbed by legacy maintenance. Competitors shipping while you are standing still.
Not every offering applies in this vertical. Here are the ones that fit, and the angle we take with each.
SaaS GenAI is product, not procurement. We build LLM-powered workflow editors, document processing pipelines, and conversational interfaces that reduce churn from feature complexity. Bedrock AgentCore in your AWS account, with the audit trails enterprise InfoSec needs.
Multi-tenant SaaS modernization needs the AK Way more than most. Test coverage so AI tools can move fast safely. Clean architecture so tenants stay isolated. Observability so you can prove the audit trail to enterprise buyers.
Flexible engineering pods across application, DevOps, data, ML, and Solution Architecture, under one retainer. Two-week mobilization. Do-with delivery so capability transfers into your team as the work is delivered.
SaaS analytics needs to span tenants without leaking across them. Cross-account, cross-region pipelines on Redshift and DMS. Replacement of vendor ETL like Hevo and Fivetran. Embedded analytics customers can trust.
AWS MAP Assess and Mobilize for SaaS workloads moving from reseller-owned tenants, on-prem data centres, or other clouds. Funded by AWS, executed by Armakuni, owned by your team at the end.
Six engagements: shipped, in flight, or scopable inside an AWS-funded discovery.
Automated tenant provisioning, federated SSO, SOC 2 program, DR with measurable RTO and RPO, and reseller enablement on the same retainer.
Senior Solutions Architects engaged in 48 hours. 600 GB PostgreSQL, EC2, RabbitMQ, Redis, and S3 moved into the client-owned AWS account in 8-10 weeks.
LLM-powered editor on Bedrock that lets administrators describe a workflow in natural language. Eliminates the editor complexity that was driving churn.
AWS-funded discovery, residency mapping, and roadmap. Reframe from migration to modernization. Mobilize phase next.
S3-triggered ingestion, Textract, Bedrock-powered extraction, OpenSearch indexing. 300-400 page legal documents processed in parallel.
Privacy-preserving data staging from Redshift through AWS Clean Rooms with PII masking, hashing, and access controls. Reusable architecture.
Named, public references in this vertical. Open the case study for engagement scope, AWS funding, and outcome.
Most engagements open with a fixed-fee, AWS-funded discovery. These three workshops are where it usually starts.
Procurement-grade controls available on request. We run engagements under these regimes routinely.
Your first call is with a solution architect, not a sales exec. The first engagement is usually an AWS-funded assessment.