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Deploint

Industry 05

Engineering depth for product and platform teams.

Deploint works with technology companies on SaaS platforms, AI products, developer platforms and the data infrastructure beneath them, as an engineering team that ships production code alongside yours.

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05 / Technology

System pattern

Engineered for

  • Multi-tenancy
  • Release velocity
  • API stability
  • Cost per tenant

Typical system flow

05 stages

  1. 01Product
  2. 02APIs
  3. 03Platform
  4. 04Data
  5. 05AI features

Focus areas

  • SaaS platforms
  • AI products
  • Developer platforms
  • Data infrastructure
  • Platform modernization

01Solutions

Product and platform engineering.

We take on the hard parts of a roadmap: the platform work, the AI features and the modernization that product teams rarely have time for.
  • 01

    SaaS

    Multi-tenant SaaS architecture with tenant isolation, metering, billing integration and per-tenant configuration and data controls.

  • 02

    AI products

    AI features built into the product with retrieval, model orchestration, evaluation suites and cost controls from the first release.

  • 03

    Developer platforms

    Internal developer platforms with golden paths, self-service environments and paved-road CI/CD.

  • 04

    Cloud-native systems

    Containerized and serverless services with infrastructure as code, autoscaling and observability.

  • 05

    APIs

    Public and partner APIs with versioning, authentication, rate limiting, documentation and SDKs.

  • 06

    Data infrastructure

    Event pipelines, warehouses and lakehouses that serve product analytics, customer-facing reporting and ML.

  • 07

    AI agents

    Agentic workflows with scoped tools, permission checks, human checkpoints and traces for every action.

  • 08

    Platform modernization

    Incremental decomposition of monoliths and migration of legacy stacks while the product keeps shipping.

Related programs

02Use cases

Where we add engineering capacity.

Common situations where product companies bring in an engineering team that can own a hard problem end to end.

  1. 01

    Shipping AI features

    Taking an AI feature from prototype to production with evaluation gates, telemetry and per-request cost visibility.

    • LLM orchestration
    • Evaluation
    • Observability

  2. 02

    Scaling a SaaS platform

    Re-architecting for growth in tenants and data volume, and for enterprise requirements such as SSO, audit logs and data residency.

    • Multi-tenancy
    • SSO / SCIM
    • Sharding

  3. 03

    Platform engineering

    Reducing lead time with self-service infrastructure, standardized pipelines and service templates.

    • Developer portal
    • Terraform
    • GitOps

  4. 04

    Public API programs

    Designing and operating APIs that partners build on, with stable contracts and clear deprecation policies.

    • OpenAPI
    • API gateway
    • SDK generation

  5. 05

    Monolith decomposition

    Extracting services from a monolith along domain boundaries using the strangler pattern and contract tests.

    • Strangler pattern
    • Event streaming
    • Contract tests

03Reference architecture

A product platform built to keep shipping.

Clear contracts between product surfaces, APIs, platform, data and AI let teams change one layer without breaking the others.

Web, mobile and integration surfaces that customers use, built on a shared design system and API layer.

  • Web apps
  • Mobile
  • Design systems

04Engineering considerations

Constraints that shape product platforms.

Technology companies move fast. The constraints below are the ones that decide whether speed holds up as the product and customer base grow.

  • Constraint 01

    Multi-tenancy

    Tenant isolation decisions shape security, cost and operability for years.

    Engineering response

    • Explicit isolation model per data tier
    • Tenant-aware observability
    • Noisy-neighbor controls

  • Constraint 02

    AI quality and cost

    AI features fail quietly and can become expensive at scale.

    Engineering response

    • Offline and online evaluation gates
    • Per-request cost and latency telemetry
    • Model routing and caching

  • Constraint 03

    Release velocity

    Engineering throughput depends on fast, safe releases.

    Engineering response

    • Trunk-based development and CI
    • Feature flags and progressive delivery
    • Automated rollback

  • Constraint 04

    Enterprise readiness

    Enterprise buyers ask for SSO, audit logs, data residency and security documentation.

    Engineering response

    • SSO and SCIM provisioning
    • Customer-facing audit logs
    • Regional deployment options

  • Constraint 05

    API stability

    Customers and partners build on your APIs. Breaking changes break their systems.

    Engineering response

    • Versioning and deprecation policy
    • Contract testing
    • Backward-compatible schema evolution

  • Constraint 06

    Technical debt

    Growth often outpaces architecture, and full rewrites stall product roadmaps.

    Engineering response

    • Incremental modernization
    • Architecture decision records
    • Fitness functions in CI

05Related capabilities

Engineering disciplines behind the work.

Technology programs draw on these capabilities, delivered by one team that designs, builds, secures and operates the system.
All capabilities

06Concept architectures

Reference architectures for related problems.

These are concept architectures: illustrative engineering references, including patterns from adjacent industries, not descriptions of client engagements.
All case studies

07FAQ

Common questions.

What engineering leaders ask us about technology systems.
01Do you work as an extension of our engineering team?

Yes. We can embed engineers in your repositories, ceremonies and on-call rotations, or take ownership of a defined workstream with clear interfaces to your team. Either way, code, documentation and decisions live in your systems.

02How do you take an AI feature to production?

We define evaluation criteria before building, run offline evaluations on representative data, ship behind feature flags with telemetry on quality, latency and cost, and keep evaluation suites running as models and prompts change.

03Can you modernize our platform while we keep shipping features?

That is the point of incremental modernization. We use the strangler pattern, contract tests and feature flags so new services replace old paths gradually, without a feature freeze.

04Which clouds and stacks do you work with?

We work across AWS, Azure and Google Cloud, on Kubernetes and serverless runtimes, and with the languages and frameworks your teams already use. We adapt to your stack rather than imposing one.

Technology engineering

Need engineering depth for your product?

Bring us the roadmap problem. We'll help architect it and build it with your team.

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