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AI development company · Engineering partner

Software engineering, extended by AI.

CodeCradle modernizes existing products, automates operational work, builds agents and knowledge systems, and takes new AI-enabled software from idea to production.

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Scroll to see what we buildCodeCradle / Since 2019

01 / Engineering history

Software engineering came first.

Since 2019, CodeCradle has delivered 30+ projects through an approximately 20-person engineering team, working with companies across the UK, US and UAE. The work behind today’s AI systems includes APIs, databases, product architecture, permissions and business logic. It has always been the work.

2019
Operating since
≈20
Engineers
30+
Projects delivered
3
Markets served

02 / Where the work starts

Start with what needs to change.

Technology follows the operating problem, not the other way around.

01

Modernize an existing product

Add a useful model capability without rebuilding the application around it.

02

Automate repetitive work

Connect systems, rules, models and review into one dependable operational path.

03

Build a new product

Design the software, model layer and production operations as one product system.

04

Connect knowledge and tools

Give compatible systems controlled access to the right context and actions.

03 / Engineering capability

Different systems. One engineering discipline.

Eight focused capabilities, connected around the software, data and workflows your company already has.

01

AI Modernization & Integration

Add useful model capabilities to established software without replacing the parts that already work.

02

AI Agents & Agentic Automation

Build bounded agents that retrieve context, use approved tools, and ask for a person when judgment matters.

03

Business Process Automation

Turn repetitive operational work into reliable workflows that combine rules, models, and review.

04

Custom AI Product Development

Take a new SaaS product, internal platform, or model-enabled feature from idea to production.

05

RAG & Enterprise Knowledge Systems

Make company knowledge retrievable, permission-aware, cited, and useful inside real workflows.

06

AI Integrations & Copilots

Embed model and tool capabilities into the products, interfaces, and systems people already use.

07

Voice & Conversational AI

Design responsive voice and conversational experiences with context, actions, and clear handoff.

08

MCP Server Development & AI Tooling

Expose approved application tools, resources, and workflows to compatible AI clients through MCP.

04 / What we can build

Capability, made concrete.

These are examples of what CodeCradle can engineer. They are not invented customer projects.

Example capability

Add a capable copilot to an existing SaaS product.

Keep the current interface, users, permissions, and business logic. Add the model only where it improves a real task.

See the engineering approach
01Existing productCurrent auth
02Context + toolsApproved actions
03Embedded copilotModel evaluation

05 / The useful shift

The model is no longer the whole product.

Useful systems connect models to software, APIs, internal knowledge and operational workflows. That integration layer is where experiments become dependable capability.

06 / Production boundaries

Capability needs control.

Not every problem needs an agent. Not every step should be probabilistic. Production systems decide where models help and make that boundary visible.

  • Deterministic rules stay deterministic.
  • Model behavior is evaluated against real tasks.
  • Permissions constrain data and action access.
  • Fallbacks and human review are designed in.
Model capabilityUseful behavior

PermissionsEvery source and action begins with an explicit access boundary.

07 / How we work

Understand the system. Then change it.

Every engagement is shaped around the product, people and constraints already in place.

  1. 01

    Frame the problem

    A focused discovery conversation maps the real outcome, current system and unknowns.

  2. 02

    Inspect the environment

    We trace data, APIs, permissions, workflows and user decisions before proposing architecture.

  3. 03

    Define the smallest sound system

    Rules, models, retrieval, tools and review points are chosen for the behavior required.

  4. 04

    Build in reviewable increments

    Working software, evaluations and integration behavior stay visible throughout delivery.

  5. 05

    Launch, observe, improve

    Production behavior informs refinements to quality, workflows, models and the surrounding product.

See how an engagement works

08 / Ways to work together

The right amount of team for the work.

09 / Insights

Useful technical thinking.

All Insights
Product engineering

Adding AI to an Existing SaaS Product: Where to Start

A practical way to find the right insertion point without rebuilding the product around a model.

MCP

MCP Servers: Connecting Existing Software to AI

How tools, resources, authentication, and permissions fit between applications and compatible clients.

Architecture

RAG, Tool Use, or Fine-Tuning? Choosing the Right Approach

Start with the behavior the system needs, then choose the smallest architecture that supports it.

Production systems

From AI Prototype to Production: What Actually Changes

The work begins when a promising demo meets permissions, evaluation, failures, cost, and real users.

10 / Common questions

Before a first conversation.

If the question is specific to your system, a 30-minute conversation is usually the fastest way to make it concrete.

01Can you add AI to an existing product without rebuilding it?

Often, yes. We first map the current application, data, APIs, permissions, and user flow. Then we add the new capability behind a clear integration boundary. A rebuild is considered only when the existing architecture genuinely prevents a safe, maintainable implementation.

02Can you work with our existing development team?

Yes. CodeCradle can own a defined workstream, collaborate with your engineers, or provide an ongoing dedicated team. Architecture decisions, interfaces, responsibilities, and review points stay visible to both teams.

03Do you build MCP servers?

Yes. We build Model Context Protocol servers that expose approved tools, resources, and application actions to compatible clients. The work includes authentication, authorization, tool boundaries, testing, deployment, and auditability.

04Are you tied to one model provider?

No. The model is selected around the use case, quality target, latency, privacy needs, cost, and deployment constraints. That may mean OpenAI, Anthropic, Gemini, or an appropriate open-source model.

05Can you work with private company data?

Yes, when the architecture and provider terms fit the required data controls. We design around access boundaries, least-privilege retrieval, retention choices, audit trails, and the company’s existing security requirements.

06Can we begin with a prototype?

Yes, when a prototype will answer a real uncertainty. It should test a specific behavior, integration, or quality threshold. It should not delay decisions that can already be made from the current system and requirements.

07How do you handle evaluation and reliability?

We define observable success criteria, build representative evaluation sets, test tool and permission boundaries, and design fallbacks for model or dependency failure. Human approval stays in the path where consequences or judgment require it.

08What happens after launch?

We can maintain integrations, review production behavior, refine evaluations and prompts, update model connections, improve workflows, and continue building the surrounding application as needs change.

A useful system starts with a real problem.

What should your software do next?

Tell us what exists, what needs to change, and where the uncertainty is. We’ll help turn it into an engineering plan.

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