DevQore

AI Services

AI that ships to production, not just to a demo

We turn AI ideas into secure, measurable systems that run reliably on your cloud. From the first workshop to monitoring in production, our engineers handle the data, integration, testing and security that most AI pilots miss.

Model-agnostic and cloud-agnostic

Built for regulated industries

Human-in-the-loop by design

Measured against your KPIs

Discuss an AI use case See an AI blueprint

What we deliver

Six ways we help you put AI to work

Start with one service or combine them into a complete programme.

AI strategy and readiness

Find the AI opportunities worth funding, and the foundations you need first.

  • Use-case discovery workshops with your teams
  • Data and technology readiness assessment
  • ROI model, prioritised roadmap and responsible-AI guidelines

A clear, funded AI plan instead of scattered experiments

Generative AI and LLM apps

Assistants and tools that work from your own documents and data, securely.

  • Retrieval-augmented (RAG) knowledge assistants
  • Summarisation, drafting and document-search tools
  • Copilots embedded in the apps your staff already use

Faster answers and less manual reading and writing

AI agents and automation

Workflows that read, decide and act across your systems, with guardrails.

  • Document processing and data extraction
  • Ticket, claim and request triage and routing
  • Human-in-the-loop approval for sensitive steps

Shorter cycle times and lower cost per transaction

Predictive analytics and machine learning

Custom models that help you act before problems and opportunities arrive.

  • Forecasting for demand, cash flow and capacity
  • Churn, risk and fraud scoring
  • Pricing and recommendation models

Decisions made ahead of the trend, not after it

MLOps and LLMOps

The engineering that keeps AI accurate, affordable and available after launch.

  • CI/CD for models and prompts, with a model registry
  • Monitoring for drift, hallucinations, latency and cost
  • Deployment on AWS, Azure or GCP, or on-premises

AI that stays reliable in production

AI quality and assurance

Test AI the way you test software, so you can launch with confidence.

  • Evaluation suites built from real cases
  • Red-teaming, bias and safety checks
  • Audit trails and evidence for compliance teams

Confidence to launch, and proof for auditors

Data foundations

Great AI starts with trusted data

Clean, connected and governed data is the base of every reliable AI system. Our Data Services team builds the pipelines, platforms and governance your AI needs.

How we engage

From first workshop to AI in production

Four phases, each ending with a clear go or no-go decision before you invest further.

01

Discover

About 2 weeks
  • Use-case workshop
  • Data readiness check
  • ROI case and roadmap
02

Pilot

4 to 6 weeks
  • Working MVP on your real data
  • Evaluation against agreed KPIs
  • Go or no-go decision
03

Scale

2 to 3 months
  • Integration with your systems
  • Security and compliance review
  • Production launch
04

Operate

Ongoing
  • Monitoring and MLOps
  • Drift and cost tuning
  • Continuous improvement

Phase lengths are typical and confirmed during Discover.

AI by industry

Use cases we know well

Technology

Model-agnostic, cloud-agnostic, chosen to fit you

Leading commercial LLMs Open-source models AWS Bedrock Amazon SageMaker Azure OpenAI Azure Machine Learning Google Vertex AI Python LangChain LlamaIndex PyTorch scikit-learn Vector databases Kubernetes Terraform

FAQ

Questions we often hear

How quickly can we see results from AI?

A focused pilot on one use case typically runs four to six weeks after a short discovery phase, with clear metrics agreed up front.

Is our data safe when using large language models?

Yes, with the right setup: private or contractually protected model deployments, access controls, redaction of sensitive fields and full logging.

Do we need perfect data before starting?

No, but you need reliable access to the data your use case depends on. Our Data Services team can prepare that foundation alongside the pilot.

Which AI platforms do you work with?

We are model-agnostic and cloud-agnostic, and choose platforms such as AWS Bedrock, Azure OpenAI or Google Vertex AI to fit your data, budget and compliance needs.

From our blog

Why Most AI Pilots Never Reach Production, and the 5-Step Fix

Read the article

Have an AI use case in mind?

Tell us about it. We’ll reply within one business day with how we’d approach it.

Contact us Email sales@devqore.io