AI Integration

Practical AI work that survives contact with production. We add LLM features, assistants, and automation in a way that protects reliability, controls cost, and gives you real outcomes (time saved, support deflection, conversion lift) instead of a demo that looks good once.

Who this is for

This is a good fit if you recognise yourself in at least one of these:

  • You have a product with real users and want to add AI without risking stability or a surprise cost spike.
  • You have a prototype, but you need a clear plan (and engineering discipline) to make it dependable.
  • Your support and ops load is growing and you want automation that actually holds up under edge cases.
  • You need a senior second opinion on build vs buy, model choice, and what is feasible within your constraints.

How AI integration works (from prototype to production)

The details depend on your product and constraints, but most successful AI work follows a pattern like this:

  1. Use case and success definition. We pick one workflow to improve and define success metrics (time saved, deflection rate, accuracy, latency, cost).
  2. Architecture and integration plan. We decide where AI fits in your system: data boundaries, permissions, fallbacks, and the safest path to ship.
  3. Build the minimal production version. We implement the feature end-to-end with guardrails, logging, and cost controls.
  4. Evaluation and iteration. We add lightweight evals and review real usage so quality doesn’t regress quietly.
  5. Operationalise. Monitoring, alerts, rate limits, and clear escalation paths when the model behaves unexpectedly.

What you get

  • Production-ready AI feature or workflow integrated into your product (not a separate demo).
  • Guardrails: fallbacks, rate limits, and safe failure modes so the product stays usable.
  • Cost controls and visibility: basic tracking so AI costs don’t become a hidden tax.
  • Evaluation approach so quality is measurable and improvements are repeatable.
  • Clear documentation of the approach and trade-offs so your team can maintain and extend it.

Reliability, security, and cost guardrails

AI integrations fail in predictable ways: silent quality drops, unexpected latency, sensitive data leakage, and runaway cost. We design with those failure modes in mind.

  • Data boundaries and permissions: least privilege by default.
  • Safe fallbacks: the workflow still works when AI is unavailable.
  • Cost caps and monitoring: prevent surprise bills as usage grows.
  • Observability: logs and metrics that make model behavior visible.

Pricing and commitment

AI work is usually structured as a short, focused sprint. Typical starting points are in the same range as other multi-week engagements: $4,000 to $8,000+, depending on scope, timeline, and complexity.

You keep all code and deliverables from each milestone. There are no long-term contracts. If it doesn’t create value, you stop.

Considering AI features in your product?

Book a free 30-min technical clarity call and we’ll outline the smallest safe path to a production-ready AI feature.

Book a free 30-min technical clarity call

FAQ

Do you build with a specific model or provider?

We choose based on your constraints (cost, latency, quality, data, and compliance). The goal is a dependable product capability, not allegiance to a vendor.

What access do you need?

Typically read access to your codebase and a way to understand the workflow and data. We can work with redacted data where necessary.

How do you prevent hallucinations and bad outputs?

We design the feature so it fails safely: constrained outputs, validations, retrieval patterns where applicable, human-in-the-loop where needed, and clear fallbacks.

Can this start with a small proof of value?

Yes. We prefer starting with one workflow and shipping a minimal production version quickly, then iterating based on usage and evals.