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Ankor

// AI Product Engineering detailed page in progress

AI features that ship like real products.

End-to-end product engineering for AI-native features — design, backend, frontend, and ML — delivered by one coordinated team.

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// Who this is for

Built for teams who are past the experiment phase.

01

Founders turning a validated AI prototype into a shippable product.

02

Product leaders adding AI-native capabilities to an existing SaaS.

03

Enterprise innovation teams spinning out AI products outside the core roadmap.

// What we deliver

The scope, in plain language.

Every engagement is scoped against your business outcome, not a fixed menu. What you see below is the typical shape — we tighten it with you in the first week.

  • Product design and UX for AI-specific patterns (chat, streaming, citations, confidence surfaces).
  • Backend engineering: inference routing, queueing, caching, rate limiting.
  • Frontend engineering: streaming UIs, tool-use surfaces, feedback loops.
  • ML/LLM engineering: model selection, prompt systems, fine-tuning where justified.
  • Infra and ops: deployment, monitoring, cost tracking, incident response.

// How we work

The Ankor 7-stage framework, applied to ai product engineering.

  1. 01
    Discover

    Align on business outcome, constraints, and success metric.

  2. 02
    Define

    Pin down scope, architecture, and the evaluation bar.

  3. 03
    Design

    Model, data, and UX design — with trade-offs on the table.

  4. 04
    Data

    Audit, remediate, and pipe the data the build actually needs.

  5. 05
    Develop

    Ship the system in small, testable increments against the eval bar.

  6. 06
    Deploy

    Rollout with shadow mode, guardrails, and rollback.

  7. 07
    Drive

    Operate, measure, and iterate — handoff or retainer.

// Outcomes you can expect

Ranges, not guarantees. Specific, not boastful.

Detailed scope and timeline available on request.

This service page is being expanded. Reach out for a scoping conversation.

One team, prototype to production.

No handoffs between design, backend, and ML — reduces rework.

Production AI in 8–16 weeks.

Depending on product surface area.

// Why Ankor

A decade of shipping software, repointed at production AI.

10
years shipping software
190+
clients delivered
260+
products shipped
800K+
daily users served

Serving clients across APAC, the US, and EMEA.

Detailed page coming soon

This service is active — we are writing the full page for a future release. In the meantime, the scope, personas, and outcomes above are accurate. Contact us for a conversation.

// FAQ

Questions we get a lot.

Is this service page complete?

No — detailed content is being expanded. The scope above is accurate; contact us for a full scoping conversation.

How is this different from your AI consulting service?

Consulting produces a roadmap and recommendations. Product engineering builds the thing. Most clients do consulting first, then product engineering; some skip straight to product engineering when scope is already clear.

Do you staff full product teams?

Yes — design, PM, backend, frontend, and ML engineering, coordinated as one pod.

// Ready to ship?

Let's talk about what to build first.

Short call. No deck. We will tell you honestly whether we are the right team for your problem.

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// Related services

Keep exploring.

AI Consulting

AI strategy that survives the roadmap review.

Opinionated roadmaps, build-vs-buy math, and vendor selection from a team that ships production AI.

AI Agent Development

Agents that actually do the work.

Multi-step agents with real guardrails, evaluation harnesses, and production observability — not demoware.