Discovery & architecture.
Define the system's boundaries, integration points, data flows, and non-functional requirements. Agree on the technology stack during discovery — not after.
Who this is for
Two engagement shapes, one engineering posture.
Sense7ai builds backend and platform systems for teams standing up new infrastructure and for teams modernising what already runs. The test coverage, observability, audit trails, and exit provisions are the same in both cases — only the shape of discovery changes. Regulated and non-regulated work are handled with the same posture; the specific compliance overlay is scoped during discovery.
Greenfield builders
Teams standing up a new backend, platform, or integration layer from scratch — discovery begins with architecture, data model, and non-functional requirements. We define the boundary before we build inside it. New builds typically run 12–24 weeks from discovery to production.
Legacy modernizers
Teams extending, integrating with, or replacing backends already in production — discovery begins with a code and architecture review, then a written replacement or extension plan. No rip-and-replace by default; we work against the substrate the business actually runs.
Less ideal
Single-feature MVPs · throwaway prototypes · open-ended time-and-materials extensions without defined deliverables · projects that need a body shop rather than an engineering team.
What we build
Backend systems and platforms built to last.
Sense7ai's custom software practice covers backend systems, APIs, compliance infrastructure, and integration layers. Every system we build treats human oversight as a design requirement — approval gates, audit trails, and access controls are scoped from day one, not retrofitted.
The governance and compliance architecture in every system we build is also the foundation regulated AI adoption requires — the audit trails, data pipelines, and access controls do not change when AI components are introduced.
DeliveredIn production for affiliated-group engagements01 / 04
Capability 01
Backend systems and API platforms.
REST and GraphQL APIs, microservice architectures, and event-driven backends engineered for reliability and observability. Human-approval gates are defined at consequential handoff points — no automated action without a policy-defined threshold.
Shipped in
- ArkOSsix-module enterprise platform
- ZitaAI-powered recruitment platform
- Infrastridereal-estate management portal
Capability offeredScope agreed during discovery
Data pipelines and integration platforms.
ETL/ELT pipelines, data lake and warehouse integrations, and real-time streaming systems connecting enterprise data surfaces. We offer this capability — scope and data residency requirements are agreed during discovery.
Regulatory reporting pipelines.
FFIEC call-report data infrastructure, HMDA and CRA reporting systems, BSA/AML data pipelines, and regulatory-filing automation with examiner-defensible output and full audit trails. We offer this capability for regulated financial institutions — specific regulatory frameworks and filing requirements are defined during discovery.
Our approach
Four phases, every engagement.
Design & spec.
API contracts, data models, service interfaces, and test plans written before build begins. Architecture reviewed by both sides before sprint one.
Build & test.
Iterative delivery in two-week sprints. Test coverage is not optional; observability is instrumented from sprint one.
Deploy & operate.
Production deployment with a documented runbook. Incident response per S7AI-RB-001. Support and on-call cadence agreed in the SOW.
Tech stack
What we work with.
We work with: Python / Go / Node.js / TypeScript; PostgreSQL / MySQL / MongoDB / Redis; AWS / Azure / GCP; Terraform / Pulumi for infrastructure-as-code; Docker / Kubernetes; GitHub Actions / CircleCI for CI/CD; Apache Kafka / AWS SQS for event-driven systems; Datadog / Grafana / OpenTelemetry for observability.
Tech-stack caveat: The specific frameworks, tools, and cloud platforms used in any engagement are agreed during discovery and reflect the customer's existing environment, integration constraints, and team preferences. The list above represents the range of tooling our engineering team works in; not every tool is used on every engagement.
Client examples
Shipped engagements.
All current production engagements are with affiliated companies within the Aeonn Ark Group portfolio. This is disclosed openly in diligence — see security.
Custom software development — common questions.
What makes Sense7ai's custom software different from a typical development agency?
We build for regulated production environments — not demos. Every system we ship includes test coverage, structured observability, documented runbooks, and audit trails. We do not use subcontractors; the team that sells the engagement is the team that builds it.
Do you build greenfield systems or work on existing codebases?
Both. We scope greenfield builds from architecture through deployment, and we take on existing codebases where the customer needs additional capacity or a specific capability. For legacy codebase engagements, we begin with a two-week discovery that includes a code and architecture review before committing to a build plan.
How do you handle data security in custom software?
Our adopted policy set (S7AI-POL-001 through 008) applies to every engagement. For financial services customers, we implement GLBA Safeguards Rule-aligned controls and FFIEC-compliant audit trails. For healthcare, HIPAA-aligned controls; for pharma, FDA 21 CFR Part 11 electronic record requirements. Data handling obligations are documented in our Data Processing Addendum, signed at engagement start.
What do you deliver for federally regulated financial institution engagements specifically?
For federally regulated financial institution engagements, Sense7ai implements GLBA Safeguards Rule-aligned access controls, FFIEC IT Examination Handbook-compliant audit trails, and U.S.-only data residency with controlled, logged access for India-based engineering personnel. For analytical or model-assisted components, our approach is informed by SR 11-7 principles — human-approval gates on consequential decisions and decision trails captured per your records-retention requirements. The same GLBA and FFIEC controls we build into software systems today are the ones that govern AI components when they are introduced — the compliance architecture does not start over at the AI boundary. Customer audit rights are in our MSA; a vendor diligence pack is available under NDA with one business day turnaround.
What is your approach to API design?
We design APIs contract-first: OpenAPI specification agreed before build begins, versioning strategy defined at the start, and breaking-change governance documented in the SOW. APIs ship with test suites covering happy paths, edge cases, and error handling. We do not deploy undocumented APIs to production.
Do you support ongoing operations after delivery?
Yes. Post-delivery support is scoped in the SOW. Options include a fixed support retainer with defined SLAs, a time-and-materials support arrangement, or a full handover to the customer's engineering team with knowledge transfer. All engagements include exit provisions from day one — code, documentation, credentials, and runbooks transfer to the customer on exit.
What is Sense7ai's pricing and engagement model for custom software work?
Engagements are fixed-scope, fixed-fee where the deliverable is well-defined (greenfield builds, discrete platform modules, integration projects), or milestone-based for longer programs scoped into 4–8 week phases. We do not run single-sprint POCs or pay-per-prompt arrangements. Typical engagement size for a production system is 12–24 weeks from discovery through deployment; smaller integration or capability-extension engagements are 4–8 weeks. Pricing is shared in writing after the scoping call once the system boundary and non-functional requirements are agreed.