Software Engineering for Custom Data and AI Platforms at Enterprise Scale
Software engineering at DS Stream means custom software platforms behind your enterprise data, AI and analytics workflows. We build bespoke data applications, AI-powered business tools, APIs and internal developer platforms, engineered for reliability, scale and ongoing evolution. Discovery takes 2-3 weeks; a production-ready MVP follows in 8-12 weeks.
Key takeaways
- SaaS tools cover 80% of enterprise needs; the last 20%, the workflows specific to your business, requires custom engineering.
- Delivery runs in three phases: discovery in 2-3 weeks, a production-ready MVP in 8-12 weeks, then scale and ownership transition over 12-24 weeks.
- DS Stream reports a 40-60% productivity gain for users of custom-built workflow tools versus spreadsheets, 3x faster delivery than typical agencies, and a 99.9% uptime SLA.
- Most projects use Python/FastAPI and TypeScript/React, deployed on your cloud (AWS/GCP/Azure) with full infrastructure as code.
What is software engineering at DS Stream?
It is the design and build of production software that powers enterprise data, AI and analytics workflows. DS Stream delivers internal data apps, AI-powered business tools, customer portals and production-grade APIs, engineered for enterprise scale with CI/CD, monitoring and support built in from day one.
Typical use cases span three areas:
- Operations - internal data apps built on your data platform
- Knowledge work - AI-powered tools embedded in daily workflows
- External - customer portals exposing data and services
Every engagement is delivered as production software, not a prototype: quality, uptime and maintainability are part of the scope.
Why can't off-the-shelf tools solve enterprise problems?
SaaS tools cover 80% of needs. The remaining 20%, the workflows specific to your business, requires custom engineering. Without disciplined software engineering, those custom solutions become maintenance burdens that nobody owns, and critical processes drift back into spreadsheets or decaying internal apps.
- Spreadsheet sprawl - critical business processes run on Excel because no custom tool exists.
- Vendor tool limits - SaaS tools cannot accommodate your specific workflows or integrations.
- Internal app decay - legacy internal apps maintained by departing engineers; nobody understands them anymore.
- Slow development - new features take quarters to ship because engineering is a bottleneck or outsourced poorly.
Who is this software engineering service for?
It is built for three roles: the Chief Data Officer who needs custom apps that unlock data value for non-technical business users, the Head of AI or Innovation who needs AI applications embedded into operational workflows, and the CTO who needs a reliable engineering partner for work internal teams cannot prioritize.
- Chief Data Officer - needs custom apps unlocking data value for non-technical business users.
- Head of AI / Innovation - needs custom AI applications embedded into operational workflows.
- CTO - needs a reliable engineering partner for projects internal teams cannot prioritize.
If your team already owns a data platform, see Data Engineering for the pipelines underneath these applications.
What technology stack do you build on?
Most projects use Python with FastAPI on the backend and TypeScript with React on the front end, in cloud-native, microservices-based architectures. We adapt to your existing stack where it makes sense, and deploy on your cloud (AWS/GCP/Azure) with full infrastructure as code.
- Backend: Python, FastAPI
- Frontend: TypeScript, React
- Architecture: cloud native, microservices
- Delivery: CI/CD, monitoring and support from day one
After launch we can operate the solution or hand it over to your internal team. For cloud platform work around the application, see Cloud Solutions.
Software Engineering Capabilities
Our capabilities cover the full span from a single internal data app to an internal developer platform for your data and AI teams, including APIs and the modernization of legacy applications.
Custom Data Applications
- Internal tools and dashboards built on your data platform
- Custom workflows for business processes SaaS tools cannot cover
AI-Powered Business Tools
- Embed LLMs and ML models into operational workflows
- Delivered as custom applications used by business teams
Platform Engineering
- Internal developer platforms for data and AI teams
- Accelerates team productivity and reuse
API Development
- Production-grade APIs exposing data and ML services
- For internal and external consumers
Modernization & Refactor
- Modernize legacy applications into cloud-native architectures
- Incremental refactoring or full rewrite, depending on the situation
How we build your software engineering practice
Three phases take an idea from user research to a scaled product owned by your team.
Phase 1 - Discovery (2-3 weeks)
User research, workflow mapping, technical architecture, and MVP scope definition.
Phase 2 - Build MVP (8-12 weeks)
A production-ready MVP, with end users testing real workflows.
Phase 3 - Scale (12-24 weeks)
Add capabilities, scale users, and transition operational ownership.
Business impact
40-60%
Productivity gain for users working in custom-built workflow tools instead of spreadsheets.
Source: DS Stream Software Engineering service page
3x
Faster delivery than typical agencies, through proven architecture patterns and reusable components.
Source: DS Stream Software Engineering service page
99.9%
Uptime SLA on production-grade solutions, with CI/CD, monitoring and support built in from day one.
Source: DS Stream Software Engineering service page
What each delivery phase covers
| Criterion | Phase 1 - Discovery | Phase 2 - Build MVP | Phase 3 - Scale |
|---|---|---|---|
| Duration | 2-3 weeks | 8-12 weeks | 12-24 weeks |
| Main focus | User research, workflow mapping, technical architecture | Production-ready MVP with end users testing real workflows | Add capabilities, scale users, transition operational ownership |
| Commercial model | Typically fixed-price | Typically fixed-price | Ongoing development on time and materials |
| Outcome | Defined MVP scope and target architecture | Working MVP used on real workflows | Scaled product and handover to your internal team |
Most common questions about software engineering
What stack do you use?
Python with FastAPI and TypeScript with React for most projects, in cloud-native, microservices-based architectures. Where it makes sense, we adapt to the stack your teams already run and maintain, so the solution stays supportable after handover.
Do you handle hosting?
Yes. We deploy on your cloud (AWS/GCP/Azure) with full infrastructure as code, so the environment is reproducible and auditable. After launch you choose the model: we can operate the solution for you, or hand it over to your internal team.
Is the engagement fixed-price or time and materials?
Phase 1 (discovery) and Phase 2 (MVP) are typically fixed-price, because scope and success criteria are defined up front. Ongoing development after the MVP runs on time and materials, which keeps the backlog flexible as users ask for new capabilities.
Can you take over legacy applications?
Yes. We modernize legacy applications through incremental refactoring or a full rewrite, depending on the situation. The target is a cloud-native, maintainable architecture, so the application no longer depends on a handful of engineers who understand the old code.
How long before we have something in production?
Discovery takes 2-3 weeks and ends with MVP scope and technical architecture. The build phase delivers a production-ready MVP in 8-12 weeks, with end users testing real workflows. If you start from the 3-day discovery workshop, the MVP target is 10 weeks.
What happens after the MVP goes live?
Phase 3 runs 12-24 weeks: we add capabilities, scale to all users, and transition operational ownership to your team. Production-grade quality is built in from day one, including CI/CD, monitoring and support, backed by a 99.9% uptime SLA.
How do we start?
Book a free 30-minute review. We assess the current state, identify wins, and outline a path to production-grade delivery. The first paid step is a 3-day discovery workshop that defines MVP scope, users and success criteria.
Related services and solutions
- Data EngineeringThe pipelines and platforms that custom data applications read from and write to.
- AI EngineeringRelevant when the custom application needs to embed LLMs or ML models into operational workflows.
- Dedicated Development TeamAn option when you need senior engineers for work your internal teams cannot prioritize.
- Cloud SolutionsCloud-native foundations and infrastructure as code for applications deployed on AWS, GCP or Azure.
- All DS Stream servicesThe full service catalog, from data platforms to MLOps and AI governance.
Ready to modernize your software engineering practice?
Book a free 30-minute review. We will assess the current state, identify wins, and outline a path to production-grade delivery. Next steps are a 3-day discovery workshop to define MVP scope, users and success criteria, then a production-ready MVP with real users testing real workflows.
Book a 30-minute consultation