How quickly can you start my project?

We typically deploy core teams within 2 weeks. Our agile process means no long discovery phase or onboarding lag — we move at startup speed with enterprise discipline.

Do you work with legacy systems?

Absolutely. Legacy modernization is one of our core strengths. We migrate monoliths to secure, cloud-native microservices without taking your business offline while we work.

How do you handle data security?

Security is built in via DevSecOps — not added at the end. We follow SOC 2 / HIPAA-aligned practices from sprint one, implement zero-trust architecture, and conduct regular penetration testing.

Who owns the code and IP?

You do. 100%. Once delivered and paid for, all source code, designs, and documentation belong specifically to your company. No licensing gotchas.

Do you provide post-launch support?

Yes. Flexible SLAs covering 24/7 monitoring, security patching, performance optimization, and ongoing feature work. We don't disappear after go-live.

What pricing models do you offer?

Fixed Bid for clear scopes (clarity and budget certainty) and Time & Materials for evolving roadmaps (flexibility to adapt). We recommend the model that protects your investment best.

Is it really free? What's the catch?

No catch. It's a 60-minute working session with a senior AI engineer — no pitch deck, no commitment. We do a handful of these every month because the conversations are how good engagements start. If we're not the right fit, you still leave with a written summary you can use.

We don't have an AI strategy yet — is this still useful?

Especially then. Most of the value is in deciding what NOT to build. We'll pressure-test the use case, sanity-check ROI, and tell you which 80% of vendor demos to ignore. For a sense of the shape of work that survives that filter, see our AI case studies.

Will you look at our actual stack and data?

Yes — bring whatever you can share. Architecture diagrams, prompts, eval traces, retrieval setup, model bills, anonymized samples. The audit gets sharper the more concrete it is. Anything you share stays under NDA and is yours.

Who shows up on the call?

A senior AI engineer who has shipped production GenAI / agent systems — not an account manager. Depending on your stack we may bring a second specialist (data, MLOps, or platform) so you get a real second opinion, not a sales hand-off.

What do I walk away with?

A short written summary covering: the highest-leverage problems we'd tackle first, the architecture choices we'd defend or change, specific risks (cost, latency, hallucination, eval, guardrails), and a rough sequencing. You can share it internally as-is.

How is this different from a generic 'discovery call'?

Discovery calls are about us learning your budget. This is about you getting a useful technical opinion. We come prepared, we go deep on one or two problems instead of skimming ten, and we're explicit about what we don't know.

Product engineering insights

Engineering essays from teams that ship daily.

How we build product at CDS: the architecture choices, team shapes, and DevEx investments that let small teams out-ship larger ones — without burning the codebase down every two years.

Explore the Product Engineering practice All practices

Essays in this series

5 guides worth your morning coffee.

Article 018 min read

Monorepo vs. Polyrepo in 2026: A Pragmatic Decision Framework

The five forces that decide it for you — team count, deploy independence, dependency graph, ownership clarity, and tooling maturity — with patterns for each path.

MonorepoArchitectureDevEx

What's inside

1The five forces that decide monorepo vs. polyrepo
2Nx, Turborepo, Bazel: when each earns its keep
3Ownership at scale: CODEOWNERS, contracts, and review
4Deploy independence inside a monorepo (it's possible)
5Migration paths in both directions

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Article 0210 min read

Shipping Production Mobile With Flutter: 7 Hard-Earned Lessons

Offline-first, native interop, deep linking, store reviews, and the test pyramid that lets a small Flutter team out-ship a large native one.

FlutterMobileOffline-first

What's inside

1Why we still pick Flutter for offline-first, transactional apps
2Native interop patterns that don't fight the framework
3Deep linking, attribution, and the messy app-link reality
4Test pyramid: widget, integration, and golden tests
5Release engineering: phased rollout and rollback

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Article 039 min read

Design Systems Are an Engineering Problem, Not a Figma Problem

Tokens, components, governance, and contribution model — the engineering choices that make a design system survive its second year.

Design systemFrontendTokens

What's inside

1Why most design systems collapse in year two
2Tokens: semantic vs. primitive, themes, and dark mode
3Component contracts and the public API discipline
4Contribution model: the federation that actually works
5Adoption metrics that don't lie

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Article 048 min read

Trunk-Based Delivery + Feature Flags: The Ship-Daily Stack

How we get from commit to production in under 30 minutes — with feature flags, preview environments, and a CI pipeline that doesn't lie about flakiness.

DevExCI/CDFeature flags

What's inside

1Why long-lived branches are the velocity tax nobody costs
2Feature flags: gates, experiments, and entitlements
3Preview environments per PR — without the bill spiking
4Quarantining flaky tests as a first-class engineering practice
5Progressive rollout, blue/green, and canary patterns

Modernize your delivery pipeline Book an audit →

Article 059 min read

AI-Native Product Design: Designing With Intelligence From Day One

The product patterns (smart defaults, progressive disclosure, drafts, copilots, agents) that turn AI from a feature into the product spine.

AI UXProduct designCopilots

What's inside

1Why bolted-on AI features under-deliver on retention
2Five AI-native product patterns we keep reusing
3Trust UI: confidence, citations, and the override path
4Latency budgets and the feel of a fast AI product
5Measuring the AI lift, not just AI usage

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The practice behind the essays

Ready to put any of this into production?

Every essay above is a play we run for clients today. Read the full Product Engineering practice page for capabilities, outcomes, engagement models, and FAQs — or skip ahead and book a free 60-minute audit.

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Other practices

More essays, other disciplines.

Browse all

Engineering essays from teams that ship daily.

5 guides worth your morning coffee.

Monorepo vs. Polyrepo in 2026: A Pragmatic Decision Framework

Shipping Production Mobile With Flutter: 7 Hard-Earned Lessons

Design Systems Are an Engineering Problem, Not a Figma Problem

Trunk-Based Delivery + Feature Flags: The Ship-Daily Stack

AI-Native Product Design: Designing With Intelligence From Day One

Ready to put any of this into production?

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