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AI Integration & Agentic Workflows

We design and ship AI agents, RAG systems and LLM integrations that plug into the software, data and workflows you already run — measured on business outcomes, not demos.
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Agentic workflow · reference architecture

Hover or focus a node to see what it does

Input

Where work arrives

AI Agent

Reasons, retrieves, plans

Tools / APIs

Systems it may use

Decision

Auto or hand-off

Outcome

Measured results

Agent core

AI Agent

The orchestration layer: it retrieves approved knowledge (RAG), applies guardrails and permissions, keeps conversation and task memory, plans the next step and calls tools with explicit scopes. Provider-agnostic — Claude, OpenAI or Gemini by configuration.

inputs tool calls to decision automated outcome human hand-off

The problem

Most AI initiatives stall between the demo and production.

Symptoms we hear in the first discovery call — usually more than one at once.

  • A chatbot that answers generically and can't act on anything in your systems.

  • Manual workflows — lead qualification, scheduling, document handling — that still eat hours every day.

  • No safe way to let an LLM use your private data without hallucinating or leaking it.

  • Uncertainty about which model, provider and architecture actually fit the job and the budget.

  • No testing discipline for non-deterministic AI behaviour, so nobody trusts it in front of customers.

Our approach

Engineering-first AI, grounded in your data and tested like software.

We treat AI as a system component: scoped, integrated with your tools and APIs, grounded in retrieval, guarded by rules and evaluated continuously. The same team that builds the product builds the AI — so it ships.

  1. 01

    Start with a feasibility and ROI review so we automate the right workflow first.

  2. 02

    Ground every answer in approved content with retrieval-augmented generation (RAG).

  3. 03

    Give agents tools — CRM, calendar, ticketing, databases — with explicit permissions and audit trails.

  4. 04

    Provider-agnostic architecture: Claude, OpenAI or Gemini behind one interface, swappable by configuration.

  5. 05

    AI/LLM test suites (golden sets, evaluations, regression) so behaviour is measured, not assumed.

What we build

Six ways we put AI to work

Each can be scoped on its own; most engagements combine two or three. Every one is grounded, guarded and evaluated the same way.

  • Claude
  • OpenAI
  • Gemini
  1. 01

    AI chatbots & customer support agents

    Grounded assistants that answer from your approved knowledge base, capture leads and hand off to people when confidence drops.

  2. 02

    Agentic workflow automation

    Multi-step agents that qualify leads, schedule, triage tickets and process documents using your existing tools and APIs.

  3. 03

    RAG systems

    Retrieval-augmented generation over documents, databases and product content — with citations, access control and freshness.

  4. 04

    AI features in existing web / SaaS products

    Summaries, recommendations, copilots and smart search embedded into the product you already run, through APIs.

  5. 05

    AI-assisted content & SEO tooling

    Editorial and research pipelines that keep humans in the loop, outputs on brand and pages citation-ready for AI answer engines.

  6. 06

    Custom LLM API integrations

    Claude, OpenAI and Gemini behind one provider-agnostic interface — with prompt management, observability and cost controls.

    • Claude
    • OpenAI
    • Gemini

Capabilities

What AI Integration covers

Scoped individually or combined into one engagement — each capability is delivered by the same senior team.

  • Core capability

    AI chatbots & customer support agents

    Grounded assistants that answer from your knowledge base, hand off to humans and capture leads.

  • Agentic workflow automation

    Multi-step agents that qualify leads, schedule, triage tickets and process documents using your tools.

  • RAG systems

    Retrieval pipelines over documents, databases and product content with citations and access control.

  • AI features in existing products

    Summaries, recommendations, copilots and smart search embedded in your web or SaaS product.

  • AI-assisted content & SEO tooling

    Editorial and research pipelines that keep humans in the loop and outputs on brand.

  • Custom LLM API integrations

    Claude, OpenAI and Gemini integrations with prompt management, observability and cost controls.

Real delivery experience

An AI career-coaching platform, built in approved stages.

QualiteSoft is the engineering partner behind the platform’s AI-driven member experience on React, Node.js and Azure: a single Career Intelligence Profile that every tool reads from, AI insights that must cite the member's own answers, and a two-profile acceptance test that proves personalisation is real.

Read the case studyRead the case study

How it was delivered

  • React / Node.js on Azure
  • Capped hours per stage
  • Written acceptance criteria
  • Evidence-based AI outputs

How we keep AI honest

Three disciplines behind every agent we ship

The difference between a demo and a system your customers can rely on.

  • Grounding (RAG)

    Answers are generated from retrieved, approved content — with citations — rather than from the model's memory. If the source is not there, the agent says so.

  • Guardrails & permissions

    Explicit tool permissions, input and output rules, confidence thresholds and audit trails decide what an agent may do on its own and when a person takes over.

  • Evaluation suites

    Golden datasets, prompt regression tests and red-team scenarios run in CI, so behaviour is measured on every change — not assumed after a good demo.

AI/LLM evaluation is also available on its own through our QA & Test Automation service .

Use cases

Where this gets used

Typical engagements by industry — each links to how we work in that sector.

  • Professional Services

    Lead qualification agent

    Reads inbound enquiries, enriches them, scores fit and books the right call — with a human review step.

  • Healthcare & Wellness

    Patient & client intake assistant

    Collects structured intake information, answers FAQs and routes to staff — with privacy controls.

  • Beauty & Personal Care

    Booking & scheduling automation

    Agents that handle reschedules, reminders and waitlists across calendar and messaging tools.

  • Real Estate & Hospitality

    Document processing

    Extract, classify and validate contracts, invoices and forms into your systems of record.

  • Startups & SaaS

    AI career coaching platform

    Profile-driven insights, recommendations and coaching flows — as built for an AI career-coaching platform on React/Node.js and Azure.

Technology stack

Tools we reach for

Chosen for longevity, your team's familiarity and long-term maintenance — never for fashion.

Models & APIs
Anthropic ClaudeOpenAIGoogle GeminiAzure OpenAI
Orchestration
Node.jsTypeScriptPythonLangChain / custom orchestrationFunction calling / tool use
Retrieval
pgvectorPineconeAzure AI SearchEmbeddings pipelines
Platform
Next.jsReactAzureAWSVercelPostgreSQL
Quality
Evaluation suitesGolden datasetsPrompt regression testsObservability & cost tracking

Process

How a AI Integration engagement runs

Stage-gated with capped hours and written acceptance criteria — you review each stage before the next begins and can stop at any boundary.

  1. 01

    Feasibility & ROI

    Identify the workflow, the data, the risks and the measurable outcome before writing code.

  2. 02

    Data & grounding

    Prepare the knowledge base, retrieval strategy, permissions and guardrails.

  3. 03

    Agent design

    Define tools, decision points, human hand-offs and failure behaviour.

  4. 04

    Build & integrate

    Implement against your APIs, product and channels with a provider-agnostic core.

  5. 05

    Evaluate & test

    Golden sets, red-team prompts, regression suites and UAT with real scenarios.

  6. 06

    Launch & improve

    Ship with monitoring, cost controls and an improvement loop on real usage.

Related industries

Where we apply this most often

Engagement models

Ways to work with us on this

  • AI Consulting
  • Fixed Project
  • Dedicated Team
Compare engagement models and pricing

Relevant work

AI Integration in practice

Projects where this service was part of the delivery.

All work

FAQs

AI Integration — questions we get asked

AI Integration

Ready to put AI to work in your business?

Tell us the goal and the constraints. A senior person replies within one business day with a clear next step.

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