Artificial Intelligence

AI Integration

Secure, well-monitored integration of LLMs and ML services into the products and workflows you already run — without a rebuild.

Discuss this capabilityBack to AI
  • Live integration in 4–8 weeks
  • Cost & usage controls built in
  • No rip-and-replace required
Overview

Intelligence added, not bolted on

Most businesses do not need a new AI product — they need the AI capability inside the product they already have. We integrate large language models and machine learning services directly into your existing applications: enriching a support tool with summarization, adding semantic search to a content library, or embedding a recommendation model into a checkout flow. The work happens around your current architecture, not instead of it.

Integration done well is mostly engineering discipline: clean API boundaries, authentication that respects your existing access model, monitoring that tells you what a model is doing in production, and cost controls that keep usage predictable as adoption grows. We treat the AI provider as one more dependency to manage carefully — with fallbacks, rate limits, and logging — rather than a black box bolted onto critical workflows.

Capability focus

  • APIs
  • OpenAI
  • Azure AI
  • Workflows
  • Discovery workshops
  • Architecture & documentation
  • Post-launch support
Offerings

What we integrate

Practical AI integration services for teams adding intelligence to live products.

LLM API Integration

Integration of providers such as OpenAI, Anthropic Claude, or Azure AI into your application, with prompt management, error handling, and fallback logic built in.

Semantic Search & Retrieval

Vector search added to existing content, product, or document libraries so users can search by meaning rather than exact keyword match.

Workflow & Automation Integration

AI steps embedded into existing business workflows — summarization, classification, extraction — wired into the systems that already run your operations.

Auth, Security & Access Control

AI features integrated behind your existing authentication and role-based access model, so model access respects the same permissions as the rest of your product.

Monitoring, Logging & Evaluation

Observability into model latency, output quality, and failure modes, with logging that lets you audit what was sent to a model and what came back.

Cost & Usage Controls

Rate limiting, caching, and usage budgets so AI feature costs stay predictable and visible as usage scales across your user base.

Process

How we run an integration

A scoped process that adds AI capability without disrupting what already works.

  1. Discovery & Feasibility

    We review your current architecture, data, and target workflow to confirm the integration is technically sound and worth the cost.

  2. Design & Provider Selection

    Selection of the right model or provider, API design, and a plan for authentication, monitoring, and cost control.

  3. Build & Integration

    The integration is built and connected to your existing systems, with automated tests and staged rollout to limit risk.

  4. Launch & Monitor

    Production rollout with monitoring and usage controls active from day one, followed by tuning based on real traffic.

Why Ramest

Why teams integrate AI with Ramest

Fits your existing stack

We work inside your current architecture and codebase rather than proposing a parallel system, so integration adds capability without adding fragility.

Full visibility into model behaviour

Logging and monitoring make it clear what data reaches a model, what it returns, and how that changes over time, rather than treating it as opaque.

Predictable running costs

Usage controls and caching are designed in from the start, so a successful feature does not translate into an unpredictable API bill.

Security-conscious integration

Authentication, data handling, and access control are extended to cover AI features with the same rigour applied to the rest of your application.

Stack

Our AI integration stack

Providers and tools we use to connect intelligence to production systems.

AI Providers
  • OpenAI
  • Anthropic Claude
  • Azure AI
  • AWS Bedrock
  • Google Vertex AI
Retrieval & Data
  • LangChain
  • Pinecone
  • pgvector
  • PostgreSQL
Backend & APIs
  • Node.js
  • Python
  • REST
  • GraphQL
Monitoring & Ops
  • Datadog
  • Grafana
  • Docker
  • CI/CD
FAQ

Frequently asked questions

What technical teams ask before integrating AI into a live product.

How much does AI integration cost?

Cost depends on scope — how many workflows the AI capability touches, the number of providers involved, and how strict your compliance requirements are. We scope every integration through a consultation call and review of your current architecture, then agree a fixed quote before work begins, offered as a fixed-scope engagement or a dedicated team. We work with businesses of every size, from adding one capability to a single workflow through to multi-system integrations, so smaller, focused projects are always welcome.

How long does an AI integration take?

A single, well-defined integration into an existing application typically takes 4–8 weeks, covering design, build, testing, and a staged rollout. Integrations touching multiple systems or requiring new data pipelines usually take 8–14 weeks, delivered in phases so the first capability reaches production early.

What does AI integration actually mean for an existing product?

AI integration means connecting a large language model or machine learning service to an application you already run, so it adds a capability such as summarization, search, or classification without replacing the surrounding system. The model becomes one more service your application calls, governed by the same authentication, monitoring, and access controls as everything else in the product.

Should we integrate an existing AI provider or train our own model?

Integrating an established provider such as OpenAI or Anthropic Claude is the right starting point for almost every business, since it avoids the cost and time of training and maintaining a model, and general-purpose models now handle most business use cases well when grounded in your own data. Training a custom model only makes sense for narrow, high-volume problems where off-the-shelf accuracy genuinely falls short.

Will this work with our current authentication and data setup?

Yes, that compatibility check is the first thing we assess during discovery, before any integration work begins. AI features are built to sit behind your existing authentication and role-based access model, and data sent to a model provider is scoped to what the feature genuinely needs, following the same data handling practices as the rest of your application.

Who manages the integration once it's live, and what about ongoing model costs?

You own the integration code and its configuration outright, and usage dashboards give your team full visibility into ongoing provider costs. We offer support retainers covering monitoring, prompt and retrieval tuning, and provider updates, though many clients are comfortable handing routine maintenance to their own engineering team after handover.

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