Can you use our private company knowledge?

Yes, with explicit source permissions, access filtering, data minimisation, and retention controls. The exact architecture depends on the sensitivity of the data and your infrastructure requirements.

How do you measure generated output quality?

We define task-specific criteria and representative test cases, then combine automated checks with human review. Regression evaluations are rerun when prompts, models, or knowledge sources change.

Will we be locked into one model provider?

Not necessarily. We separate workflow logic from model calls where practical and can route different tasks to different models based on quality, privacy, latency, and cost.

Can generative AI be added to our current app?

Yes. We can integrate focused AI features into existing web, mobile, SaaS, ecommerce, and internal products without rebuilding the entire platform.

Generative AI Development

Generative AI products built for useful, governed output

We turn approved business knowledge and workflows into dependable text, image, document, and multimodal experiences—measured for quality, cost, and safety.

Discuss your AI project View our work

Governed AI delivery for teams worldwide

Selected brands and delivery experience

Services

What we build with Generative AI Development

AI copilots

Role-specific assistants that help teams draft, analyse, and decide with approved context.

Role-aware prompts
Knowledge grounding
Human review

Document intelligence

Extract, classify, summarise, compare, and transform complex documents.

Structured extraction
Document comparison
Validation rules

Content operations

Generate controlled first drafts and variants while protecting brand and approval standards.

Brand guidance
Reusable workflows
Approval routing

Multilingual experiences

Localise content and customer journeys with terminology and review controls.

Glossary grounding
Locale workflows
Quality review

Research workspaces

Synthesize approved sources into structured, citation-led working material.

Evidence trails
Comparison views
Exportable outputs

Product AI features

Embed generation, summarisation, and assistance inside existing digital products.

Streaming UX
Usage controls
Feedback loops

Architecture

Generative AI Development architecture for safe production delivery

400+

Projects

Years

20+

Markets

Production-gradeBuilt to scale

Experience layer

A focused interface designed around the user task rather than a generic chat box.

Structured inputs
Streaming states
Editable outputs

Prompt and workflow layer

Versioned instructions, reusable steps, and deterministic validation around model calls.

Prompt versioning
Output schemas
Fallback paths

Knowledge layer

Approved sources, metadata, permissions, and freshness rules give generation the right context.

Source controls
Access filters
Freshness checks

Model gateway

Routes tasks to suitable models based on quality, latency, privacy, and cost requirements.

Model routing
Usage limits
Provider flexibility

Evaluation layer

Tests accuracy, relevance, tone, policy compliance, and task completion before scale.

Golden datasets
Human scoring
Regression tests

Governance layer

Logs usage, protects sensitive data, and keeps accountable humans in high-impact workflows.

Audit history
PII controls
Approval roles

Process

How we deliver Generative AI Development

A clear, senior-led process from the first strategy call to launch, so every build ships on time and turns into measurable outcomes.

4 steps, one accountable team from start to launch.

Step 01

Discover and prioritise

Map the workflow, users, data boundaries, failure cost, and the business outcome before selecting any model or platform.

Step 02

Prototype with real inputs

Validate the highest-risk decisions against representative data, edge cases, and human review requirements.

Step 03

Integrate and govern

Connect approved systems with permissions, audit logs, fallbacks, observability, and clear ownership.

Step 04

Launch and improve

Release in controlled stages, monitor quality and cost, then expand only when the workflow proves dependable.

Production AI System

Designed to operate,
not just demo

Real AI value comes from dependable integration, controlled behaviour, measurable quality, and an operating model your team can own. We prioritise clearer intent flow, faster decision-making, and measurable improvement across high-value pages.

Every experience is engineered on a modern, proven technology stack, so your product stays fast, secure, and easy to scale. From front-end frameworks to databases and DevOps, we pick the right tool for each layer and keep the codebase clean and maintainable.

Clear business objective

Governed data and access

Evaluation before scale

Clean, maintainable code

Mobile-first & responsive

SEO-ready architecture

Secure and scalable by design

Plan your AI system

Generative AI Development solution blueprints

Outcomes backed by shipping quality and measurable growth

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Knowledge work

Secure team copilot

A role-aware workspace for drafting, analysis, summarisation, and reusable business workflows.

Approved contextReusable flowsReview controls

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Document operations

Document intelligence pipeline

Turns incoming files into validated structured data, summaries, comparisons, and review tasks.

Structured outputValidation rulesException queue

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Product experience

Embedded generative feature

Adds task-specific generation to an existing web or mobile product with feedback and usage controls.

Native UXUsage telemetryQuality loop

Why teams choose Solvinex for Generative AI Development

Performance is non-negotiable

We combine product thinking, engineering, integration, evaluation, and governance in one practical delivery system.

Quality before scale

Evaluated output

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Quality before scale

Evaluated output

Representative test sets and review criteria define whether the system is actually useful.

How we deliver it

Task-specific evalsPlanned, validated, and documented with clear ownership before the work moves forward.
Regression checksPlanned, validated, and documented with clear ownership before the work moves forward.
User feedbackPlanned, validated, and documented with clear ownership before the work moves forward.

We agree the baseline, success signals, review checkpoints, and handover requirements so this outcome remains measurable after launch.

Secure context

Permission aware

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Secure context

Permission aware

Knowledge retrieval respects access boundaries and avoids exposing unrelated information.

How we deliver it

Source permissionsPlanned, validated, and documented with clear ownership before the work moves forward.
Data minimisationPlanned, validated, and documented with clear ownership before the work moves forward.
Retention controlsPlanned, validated, and documented with clear ownership before the work moves forward.

We agree the baseline, success signals, review checkpoints, and handover requirements so this outcome remains measurable after launch.

Cost visibility

Usage controlled

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Cost visibility

Usage controlled

Token use, model routing, caching, and limits are designed into the operating model.

How we deliver it

Cost dashboardsPlanned, validated, and documented with clear ownership before the work moves forward.
Efficient routingPlanned, validated, and documented with clear ownership before the work moves forward.
Budget alertsPlanned, validated, and documented with clear ownership before the work moves forward.

We agree the baseline, success signals, review checkpoints, and handover requirements so this outcome remains measurable after launch.

Human-centred UX

Designed for adoption

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Human-centred UX

Designed for adoption

Users can understand, edit, verify, and approve output instead of blindly accepting it.

How we deliver it

Source visibilityPlanned, validated, and documented with clear ownership before the work moves forward.
Editable draftsPlanned, validated, and documented with clear ownership before the work moves forward.
Clear uncertaintyPlanned, validated, and documented with clear ownership before the work moves forward.

We agree the baseline, success signals, review checkpoints, and handover requirements so this outcome remains measurable after launch.

Choose the right engagement

GenAI Discovery Sprint

Use-case and data review
Prototype experience
Evaluation plan
Production roadmap

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Production GenAI Build

Application and workflow development
Knowledge integration
Evaluation suite
Secure production launch

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GenAI Optimisation

Quality monitoring
Prompt and model updates
Cost optimisation
New workflow releases

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Testimonials

AI outcomes from teams automating real work

Deployed a multi-intent support chatbot with escalation routing, qualification logic, and CRM sync, so repetitive queries stopped eating the team's day and sales-ready conversations surfaced faster.

Support Automation

AI Chatbot Implementation

Response speed +61%, support load -34%

Built an AI-assisted SEO operations workflow that flags technical issues and drafts fixes, cutting the gap between finding a problem and shipping the fix.

AI SEO Operations

SEO Automation

Fix turnaround -42%, organic leads +33%

Set up an AI blog generation pipeline with review checkpoints, lifting publishing output sharply without letting quality slip through the cracks.

Content Pipeline

AI Blog Generator

Posts/month +220%, publish cycle -58%

Built an AI landing-page generation flow that spins up test-ready pages fast, so conversion experiments launch in a fraction of the usual time.

Landing Automation

AI Website Generator

Launch speed +64%, conversion uplift +26%

Generative AI Development FAQs

We build task-specific copilots, document intelligence systems, content workflows, research workspaces, multilingual experiences, and generative features embedded inside existing products.

Generative AI products built for useful, governed output