Generative AI Integration Services

Generative AI integration for the product your users already trust.

Generative AI integration adds a bounded text, image, audio, or multimodal capability to existing software. We preserve the product's users, permissions, data, and workflow while adding provider selection, context, evaluation, review, fallbacks, monitoring, and cost controls. The first release proves one useful feature before AI spreads across the roadmap.

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

Evidence and scope

12 weeks

Recorded integration delivery

A Claude layer was added to an existing remote-patient-monitoring platform in 12 weeks.

20%

Recorded operating outcome

Project records attribute a 20% reduction in clinical decision time to that workflow.

$15K+

Focused first feature

One generative capability with evaluation, controls, monitoring, and handover.

Evidence · planning contextSee the work

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Does a live product need drafting, summarisation, retrieval, extraction, image, or voice capability without a full rebuild?

02

Are teams comparing model demos without measuring quality, latency, unit cost, provider risk, or the changed user workflow?

Plain answer

Generative AI integration adds a bounded text, image, audio, or multimodal feature to software that already has users and workflows. RaftLabs selects the provider, connects approved context and tools, evaluates representative cases, designs review and fallbacks, and monitors quality and cost. A focused first feature starts at $15,000 and usually takes six to ten weeks.

The new model feature inherited the old product's promises.

Users already trusted the application to protect their data, preserve their work, and return the same record after a retry. Adding generation did not suspend those promises. The feature still needed role permissions, a review path, a stable output shape, recovery from provider failure, and a way to see whether it saved time.

Integration succeeds when AI behaves like part of the product.

Recorded integration delivery and commercial scope

12 weeks
AI layer added to an existing platform
Remote patient monitoring project record
20%
reported reduction in clinical decision time
Customer project record
$15K+
focused first-feature start
One bounded integration

The remote patient monitoring case study records a Claude-powered layer added through AWS Bedrock in 12 weeks and attributes a 20% reduction in clinical decision time to the delivered workflow. These figures come from retained project records and are not independently audited. The project does not predict another feature's impact, and its healthcare controls must not be generalised to a different system.

Add one useful feature to a product that already has strong foundations.

The first release should improve a named journey without turning the whole roadmap into an open-ended AI program.

A fit
01

A live product already has users, permissions, data, workflows, and a team that owns operations.

02

One generative task has representative inputs, a measurable baseline, and people who can judge outputs.

03

The organisation can approve providers, data handling, review rules, and a production operating budget.

Not a fit
01

There is no existing product; choose generative AI development for a net-new experience.

02

The only requirement is a provider-specific language-model API connection; use ChatGPT, Claude, or LLM integration.

03

A built-in capability from the current software vendor already meets the need at lower total cost.

Which generative AI path fits?

DecisionGenerative AI integrationGenerative AI developmentLLM integration
Starting pointExisting productNet-new productExisting architecture needing a language-model layer
Primary scopeOne user-facing or operational featureFull product journey and operating modelContext, tools, routing, evaluation, and fallback
ModalitiesText, image, audio, or multimodalAny generative product experiencePrimarily language-model capabilities
Success evidenceChanged product outcome and technical qualityProduct use, value, reliability, and economicsModel quality, latency, reliability, and unit cost

Scope

What belongs in the first integration

  • 01
    Product and outcome design
    Choose the user journey where generation removes real work or improves the experience. Define the baseline, acceptance measure, correction path, and what the first release deliberately leaves unchanged.
  • 02
    Provider and context layer
    Evaluate approved providers on the task, assemble only authorised context, preserve source identity and permissions, decide whether retrieval or adaptation is needed, and record an exit path.
  • 03
    Output and tool controls
    Constrain formats, validate generated data, keep business rules in code, expose narrow tools, protect consequential writes, and show evidence or uncertainty to the user where the task requires it.
  • 04
    Evaluation and human review
    Test representative inputs, hard cases, affected groups where relevant, unsupported requests, access abuse, and provider failure. Size review and escalation against the queue the organisation can operate.
  • 05
    Reliability and economics
    Monitor quality, latency, cost, provider errors, review rate, user correction, and downstream outcomes. Add caching, queues, rate controls, fallbacks, rollback, and change evaluation where needed.

How it works

From product constraint to measured AI feature

  1. Phase 1
    01

    Choose the feature boundary

    Map the user, current journey, desired outcome, baseline, representative inputs, approved data, acceptance threshold, and systems the feature may affect.

  2. Phase 2
    02

    Select the provider and controls

    Compare models on the task, design context and tools, enforce permissions, define review and fallback, estimate unit economics, and record portability needs.

  3. Phase 3
    03

    Integrate and evaluate

    Create the feature inside the existing product, test representative and prohibited cases, validate failure handling, and measure quality, latency, review load, and cost.

  4. Phase 4
    04

    Release and expand deliberately

    Roll out by cohort, observe production outcomes, fix failure classes, document changes, transfer runbooks, and add another use case only after the first holds up.

Risk

What to settle before the feature reaches users

Workflow displacement
Decide how the user's job changes, where correction and approval happen, and whether the feature creates more review work than it removes.
Model dependence
Keep the evaluation set, application policy, and data contracts portable. Document the cost and functional gaps involved in changing providers.
Unbounded context or tools
Minimise retrieved data, enforce role access, whitelist typed tools, validate arguments, and require confirmation before consequential system changes.
Invisible regression
Version prompts, models, retrieval, and schemas. Run acceptance evaluations before change, release gradually, and monitor output quality alongside latency and cost.

Scope and price

A focused generative AI integration starts at $15,000.

Start with one product journey, representative cases, an approved provider set, controlled context and tools, evaluation, monitoring, and handover.

The first feature should prove a product outcome and operating cost before a second use case is added.

Starting investment

Starts at $15,000

A focused first feature usually takes six to ten weeks. Several modalities, high availability, regulated evidence, or substantial host-product changes can extend the scope.

The host product stays recognisable

The integration preserves existing identity, permissions, data ownership, and operational responsibilities rather than creating a detached AI island.

Change is measured

Prompts, models, retrieval, schemas, and tools are versioned and checked against representative cases before broad release.

Related generative AI paths

Generative AI integration questions

It adds a generative capability to software that already has users, data, permissions, and workflows. The feature may draft, summarise, retrieve, classify, transform, generate media, or assist a conversation. Production integration also needs provider selection, context controls, evaluation, review, failure handling, monitoring, and unit-cost management.

Integration extends an existing product and works within its architecture, identity, data, and operations. Development creates a net-new generative AI product with its own user journey and foundations. If the need is strictly a language-model layer rather than the broader product feature, the LLM integration page is more precise.

We compare approved options against the actual task, representative data, output quality, modality, latency, context, tool support, privacy needs, geographic availability, service limits, operating cost, and exit path. We do not choose a provider from a generic leaderboard or assume one model remains best after future releases.

We minimise context, enforce application permissions before retrieval, use narrow typed tools, validate model output, keep policy and consequential writes in deterministic code, and require review where failure matters. Logging follows the agreed data boundary. Representative tests include access abuse, prompt injection, unsupported claims, and provider failure.

A focused first feature starts at $15,000 and usually takes six to ten weeks. Several modalities, complex retrieval, multiple tools, large evaluations, high availability, regulated data, or major host-application changes can increase scope. We define the feature, acceptance evidence, assumptions, and fixed price before development starts.

Work with us

Bring the product journey where one generative feature could earn its place.

Share the user, existing product, representative inputs, expected output, approved context, provider constraints, review boundary, traffic, and failure consequence. We will scope the narrowest useful integration.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.