Custom AI Solutions & Cloud Infrastructure

Built for your needs, on the cloud you already trust

We design and build custom AI applications — and the secure, scalable infrastructure underneath them — on Microsoft Azure, Amazon Web Services and Google Cloud.

When off-the-shelf isn’t enough

Packaged AI tools are a good start, but many organizations hit their limits quickly: the data lives in systems the tool can’t reach, the workflow is specific to the business, or security rules require everything to run inside the organization’s own cloud.

That’s where a custom solution pays off. We build AI applications around your processes and data, deployed in your cloud account, under your identity and security controls — so you own the result and nothing is locked inside a vendor’s platform.

Engineering, not experiments

A demo that works once is easy; a system people rely on every day is not. We treat AI features like any other production software: versioned, tested against real examples, monitored, and deployed through automated pipelines.

Bryant Technologies has delivered enterprise software for many years. Our AI work applies the same discipline — requirements, quality assurance and documentation — to a new generation of tools.

A typical architecture

Channels

  • Web & mobile apps
  • Teams or Slack
  • APIs & integrations
Requests

AI application

  • Orchestration & agentsSteps, tools and approvals
  • Retrieval (RAG)Finds the right sources
  • GuardrailsPolicies, redaction, evaluation
Prompts & queries

Models & data

  • Foundation modelsAzure OpenAI, Bedrock or Vertex AI
  • Search indexVector and keyword search
  • Your dataDocuments, databases, APIs
Cloud foundation
  • Identity & SSO
  • Secrets & keys
  • Private networking
  • Monitoring & cost
  • Infrastructure as code
Applications sit on a shared cloud foundation; models can be swapped without changing the application or the data layer.

What we build

  • Assistants & agents

    Internal and customer-facing assistants that answer from your data and carry out approved tasks.

  • Search over your knowledge

    Retrieval-augmented generation (RAG) over documents, policies and records, with answers that cite their sources.

  • Document processing

    Extract, classify and summarize forms, contracts, invoices and correspondence at scale.

  • AI inside your applications

    Summaries, drafting and natural-language search added to the web and line-of-business apps you already run.

  • Voice

    Speech-enabled assistants for phones, kiosks and websites.

  • Questions over data

    Ask questions of operational data and data warehouses in plain language, within the permissions people already have.

On all three major clouds

We usually recommend the cloud you already use: your identity, security policies, data and contracts are there. Where there’s a real choice, we weigh data residency, model availability and cost with you.

  • Microsoft Azure

    A natural fit for organizations on Microsoft 365 and Entra ID.

    • Azure OpenAI
    • Azure AI Search
    • Azure Functions & Container Apps
    • Microsoft Entra ID & Key Vault
    • Bicep and Terraform

  • Amazon Web Services

    Broad model choice through Bedrock, next to existing AWS workloads.

    • Amazon Bedrock
    • Amazon SageMaker
    • AWS Lambda & Step Functions
    • IAM & Secrets Manager
    • AWS CDK and Terraform

  • Google Cloud

    Gemini models and BigQuery analytics for data-heavy work.

    • Vertex AI & Gemini
    • Vertex AI Search
    • Cloud Run & Cloud Functions
    • BigQuery
    • IAM, Secret Manager and Terraform

Infrastructure done properly

  • Infrastructure as code

    Repeatable environments defined in Terraform, Bicep or AWS CDK, deployed through CI/CD pipelines — no hand-built servers.

  • Private by design

    Private networking, keys in managed secret stores and data kept in the regions you choose.

  • Identity & access

    Single sign-on and role-based access through Entra ID, AWS IAM or Google Cloud IAM.

  • Monitoring & cost control

    Logging, alerting, usage limits and per-feature cost tracking for every model and service.

  • Model choice

    Models from OpenAI, Anthropic, Google and others behind one interface — switch without rebuilding the application.

  • Data pipelines

    Ingestion and preparation of the data your AI depends on, kept current automatically.

Keeping costs predictable

AI costs scale with usage, which can surprise teams that are used to fixed licences. We design for cost from day one: the right-sized model for each task, caching of repeated work, usage limits per user or feature, and dashboards that show what each capability costs to run.

How we work

  1. Assess

    Identify the use cases worth doing, the data they need and the constraints that apply.

  2. Prototype

    Prove the approach on real data, quickly, before committing to a full build.

  3. Build

    Production application and infrastructure, tested, documented and repeatable.

  4. Operate

    Monitoring, model updates and support — run by us or handed over to your team.

Related: Private AI Cloud covers fully isolated AI environments in your own cloud account, and Knowledge Base & RAG covers search over your company’s knowledge.

Need specialists on your own team? We also place experienced cloud and AI professionals — see IT Employers.

Contact us

Let’s talk about your project

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