Hire · AI integration

Hire an AI integration developer for features that earn their place

I wire OpenAI and LangChain-class tooling into existing products for a specific, scoped job — document chat, extraction, or anomaly detection — instead of shipping a vague "AI-powered" label with no clear output. GPX’s risk-insight layer is a live example.

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What you get

AI scoped to a defined output (flag anomalies, extract data, answer questions) — not a demo
Integrated into an existing data model and UI, not a bolted-on chat widget
Cost-aware: token usage and API cost modeled before launch, not discovered after
Proven pattern: GPX’s AI-driven anomaly insight on top of GPS/BLE tracking data

How engagement works

Define the specific job the AI feature must do and what "wrong" looks like.
Prototype against real data before committing to a UI or a cost model.
Integrate into the existing product — auth, data access, and UI — not a separate tool.
Add monitoring for cost and failure modes before calling it launched.

Speech-to-text and voice features (Whisper)

Founders searching for Whisper integration usually need transcription into an existing web or mobile product — not a standalone demo. Cost drivers include audio length, language, diarization, storage, and privacy (where audio is processed and retained).

TODO(owner): confirm Whisper / speech-to-text experience and add one real shipped example (product name + what was transcribed). Until confirmed, treat Whisper as a scoped capability discussion, not a published case study.

Define the exact output (transcript, timestamps, speaker labels)
Decide client vs server processing and retention policy
Budget API or self-host ops cost before launch
Read the Whisper speech-to-text integration guide for pitfalls

Who it's for

Best fit for US, UK, and Australia founders adding one well-defined AI feature to an existing SaaS or product — including speech-to-text when the use case is clear. GPX’s anomaly-insight layer is a live reference for “AI that earns its place.”

Proof

Live products you can open before you brief a build. Full list on the work / portfolio page.

MealApp — Food ordering, reservations, and payments
SwecoRide — Sweden ride-share with identity and payouts
The CareMD — Telehealth / virtual care web app
GPX — GPS / AI supply-chain tracking

Pricing

Brand & landing work often starts near $499. Multi-page business sites commonly from $1,299. Full-stack web apps from $2,999+. Custom SaaS from $4,999+ depending on scope. Every quote is itemized in USD — see packages on the services & pricing page.

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Ready to brief a build?

Send goals, timeline, and rough budget — you'll get a clear scope and USD quote. Or compare packages on the services page. See live proof on the work page.

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FAQ

What AI tooling do you use?

OpenAI and LangChain-class tooling, chosen based on the job — document chat, extraction, or pattern/anomaly detection — not a one-size-fits-all chatbot.

Can you add Whisper speech-to-text to our product?

TODO(owner): confirm Whisper delivery history. In principle, speech-to-text is scoped like any AI feature: clear output, privacy rules, and cost model before build.

Can you add AI to our existing product?

Yes — that is the usual case. The AI feature is scoped against your existing data model and UI rather than built as a separate tool bolted on the side.

How do you control AI API costs?

Token usage and expected call volume are modeled before launch, with monitoring in place so cost surprises show up early, not on the first invoice.

What does an AI integration project cost?

A scoped single feature (e.g. document chat or data extraction) often fits the full-stack band from $2,999+. More complex pipelines or multi-feature AI work scope into custom SaaS from $4,999+.

Hire an AI integration developer for features that earn their place

What you get

AI scoped to a defined output (flag anomalies, extract data, answer questions) — not a demo

Integrated into an existing data model and UI, not a bolted-on chat widget

Cost-aware: token usage and API cost modeled before launch, not discovered after

Proven pattern: GPX’s AI-driven anomaly insight on top of GPS/BLE tracking data

How engagement works

Define the specific job the AI feature must do and what "wrong" looks like.

Prototype against real data before committing to a UI or a cost model.

Integrate into the existing product — auth, data access, and UI — not a separate tool.

Add monitoring for cost and failure modes before calling it launched.

Speech-to-text and voice features (Whisper)

Define the exact output (transcript, timestamps, speaker labels)
Decide client vs server processing and retention policy
Budget API or self-host ops cost before launch
Read the Whisper speech-to-text integration guide for pitfalls

FAQ

What AI tooling do you use?

OpenAI and LangChain-class tooling, chosen based on the job — document chat, extraction, or pattern/anomaly detection — not a one-size-fits-all chatbot.

Can you add Whisper speech-to-text to our product?

TODO(owner): confirm Whisper delivery history. In principle, speech-to-text is scoped like any AI feature: clear output, privacy rules, and cost model before build.

Can you add AI to our existing product?

Yes — that is the usual case. The AI feature is scoped against your existing data model and UI rather than built as a separate tool bolted on the side.

How do you control AI API costs?

Token usage and expected call volume are modeled before launch, with monitoring in place so cost surprises show up early, not on the first invoice.

What does an AI integration project cost?

A scoped single feature (e.g. document chat or data extraction) often fits the full-stack band from $2,999+. More complex pipelines or multi-feature AI work scope into custom SaaS from $4,999+.