DTL Generative AI & Data
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Generative AI & data · Amazon Bedrock · Azure OpenAI · Vertex AI

AI that reaches production, not just a pilot.

We help you pick the AI use case worth building, get your data ready, and build assistants, search and agents that run on your own cloud, with guardrails, costs you can plan and a metric that proves it works.

Book an AI readiness assessment See what we build
  • 8Client AI solutions built on Amazon Bedrock
  • US$90–130A month to run EasyGift’s AI platform
  • SecondsFrom a photo to similar products for Spotem

The reality

Why do AI pilots stall?

The demo is the easy part. Most AI projects fail on the decisions made before the build, and on who runs it after. Open a card to see what we do about each.

Talk it through with an AI engineer

Use cases picked by enthusiasm

Every team has an idea. The loudest one gets built, not the most valuable.

What we do

We score use cases on value, effort, data and risk, so the first build is the one most likely to pay back.

Data that isn’t ready

Scattered, messy or ungoverned data gives the AI wrong answers.

What we do

We check data before we build, and fix the foundations the use case needs.

Security added too late

Guardrails and access controls are an afterthought, and the launch stalls in review.

What we do

Guardrails, access controls and logging are designed in from the first sprint.

No measure of success

Nobody agreed what “working” means, so nobody can prove it does.

What we do

We agree a target metric and a test set before building, and measure against them.

Agents with no limits

Agents get broad access and no audit trail, so nobody dares let them act.

What we do

Scoped permissions, human approval for risky steps and a full log of what the agent did.

Nobody owns it after launch

Quality drifts, costs creep and the pilot quietly dies.

What we do

We monitor quality, cost and usage after launch and keep improving the system.

What we build

From first use case to AI in production.

Five areas, from choosing what to build to running it every day, on Amazon Bedrock, Azure OpenAI and Google Vertex AI. Pick an area to see what’s included and the tools we use.

Book an AI readiness assessment
01 · AI readiness assessment

A scored plan, not another slide deck.

Know what to build first, and whether you’re ready. A short, fixed-scope assessment that tells you which use case to build first, whether your data and cloud are ready and which model fits.

DiscoverScoreTest modelsPlan
Use case prioritisationCandidate use cases scored on value, effort, data and risk.
Readiness scoreCloud, data, security and team readiness, scored.
Model evaluationModels tested head to head on accuracy, speed and cost, on your data.
Draft architectureHow the first use case would be built on your cloud.
Business caseExpected value, build cost and running cost.
Phased roadmapWhat to build first, and the next steps after it.
Tools we use
AWS
  • Amazon Bedrock model evaluation
  • Amazon SageMaker
  • AWS Well-Architected (GenAI lens)
Microsoft Azure
  • Azure AI Foundry evaluations
  • Azure OpenAI
  • Azure Well-Architected (AI)
Google Cloud
  • Vertex AI evaluation
  • Model Garden
  • Google Cloud Architecture Framework
02 · Generative AI applications

Useful AI features, in your own cloud.

Search, assistants and documents, built on your data. Search, assistants, document processing and content generation, built on your data and kept inside your account.

DesignGroundBuildEvaluateShip
Search by meaning or photoSemantic and visual search across catalogues and documents.
Assistants and chatbotsFor staff or customers, on web, Teams or WhatsApp, grounded in your content.
Document processingInvoices, KYC and forms read, checked and filed.
Quotes and proposalsOn-brand quotes and documents generated from your rules.
Retrieval-augmented generationAnswers drawn from your own documents, with sources.
Process automationProcurement, vendor follow-up and other manual workflows automated.
Tools we use
AWS
  • Amazon Bedrock
  • Amazon Bedrock Knowledge Bases
  • Amazon Titan Embeddings
  • Amazon Textract
Microsoft Azure
  • Azure OpenAI
  • Azure AI Search
  • Azure AI Document Intelligence
  • Azure AI Foundry
Google Cloud
  • Vertex AI
  • Vertex AI Search
  • Document AI
  • Gemini models
03 · Agentic AI

Agents you can trust to act.

Agents that act, within limits you set. Agents that plan, use your tools and data, and complete multi-step work, with human approval where it matters and a log of every step.

ScopeConnectLimitTestRun
Agents that investigatePlan, query, analyse and explain, like our QueryDash platform.
Tool and system accessConnected to your apps and data through APIs and MCP.
Scoped permissionsEach agent can only do what it’s allowed to.
Human in the loopApproval before risky or irreversible actions.
Memory and contextFollow-up questions keep the context of the task.
Multi-agent workflowsSpecialist agents working together, with clear hand-offs.
Tools we use
AWS
  • Amazon Bedrock AgentCore
  • Amazon Bedrock Agents
  • Strands Agents
  • Model Context Protocol (MCP)
Microsoft Azure
  • Azure AI Foundry Agent Service
  • Semantic Kernel
  • Azure Logic Apps
  • Model Context Protocol (MCP)
Google Cloud
  • Vertex AI Agent Engine
  • Agent Development Kit
  • Agent2Agent (A2A)
  • Model Context Protocol (MCP)
04 · Data foundations

Data your AI can trust.

The data your AI and reports depend on. Pipelines, warehouses and vector stores that give AI and reporting clean, connected, governed data.

IngestCleanStoreGovernServe
Data pipelinesData from your ERP, CRM, apps and files, on a schedule or near real time.
Warehouse and lakeOne central store for reporting and AI.
Vector storesEmbeddings in pgvector, OpenSearch or AI Search for semantic retrieval.
Data qualityChecks so the AI isn’t built on bad data.
GovernanceAccess controls and a catalogue, so people know what to trust.
DashboardsReports that refresh on their own, in the BI tool you use.
Tools we use
AWS
  • AWS Glue
  • Amazon Redshift
  • Aurora PostgreSQL with pgvector
  • Amazon OpenSearch
Microsoft Azure
  • Azure Data Factory
  • Microsoft Fabric
  • Azure AI Search
  • Power BI
Google Cloud
  • BigQuery
  • Dataflow
  • AlloyDB
  • Looker
05 · AI operations and governance

AI that keeps working after launch.

Quality, safety and cost, watched after launch. We test before release, watch quality and cost in production, and keep improving the system as people use it.

EvaluateGuardWatchImprove
Evaluation setsA fixed set of test questions with known answers, run before every release.
GuardrailsPrompt-injection defence, content filters and personal data redaction.
Cost controlModel choice, caching and limits keep cost per request planned.
MonitoringQuality, usage, cost and drift tracked, with alerts.
Release pipelineVersioned prompts, models and agents, with a way back.
Continuous improvementFeedback from real users turned into better answers.
Tools we use
AWS
  • Amazon Bedrock Guardrails
  • Amazon CloudWatch
  • Bedrock model invocation logging
  • AWS Secrets Manager
Microsoft Azure
  • Azure AI Content Safety
  • Azure Monitor
  • Azure AI Foundry tracing
  • Azure Key Vault
Google Cloud
  • Model Armor
  • Cloud Monitoring
  • Vertex AI model monitoring
  • Secret Manager

We pick models and tools for your use case and work with the cloud you already use.

What you get

AI built like a product, not a demo.

What changes when AI is chosen on evidence, built for production and run after launch.

Build the right thing first.

Use cases are scored on value, effort, data and risk, and models are tested on your data, so your first build is the one most likely to pay back.

TodayThe loudest idea gets built
With usA scored use case and a tested model

Data that’s ready

Gaps found and fixed before the build, not after.

Governance from day one

Guardrails, access and audit logs designed in.

A metric agreed up front

Success defined and measured, so you can prove it.

Running costs you can plan

Model choice and design keep cost per request in check.

Your data stays yours

Built in your own cloud account, in Indian regions where you need it.

Why AI fails after the demo

The model is rarely the problem. The decisions around it are.

These are the reasons AI projects stall between demo and production, and what we do about each.

The riskThe model picked by reputation
How we prevent it

Models are tested head to head on your data for accuracy, speed and cost before we choose.

The riskA demo that never meets real data
How we prevent it

The pilot runs on real data and real users from the start.

The riskAnswers nobody can check
How we prevent it

A fixed evaluation set is run before every release, and answers cite their sources.

The riskCosts that grow with every user
How we prevent it

Cost per request is designed, measured and watched from the first build.

The riskAgents that do too much
How we prevent it

Permissions are scoped and risky actions need a person to approve them.

The riskA launch with no one behind it
How we prevent it

We monitor, support and improve it after go-live, or hand it to your team with runbooks.

Every release can be rolled back. Prompts, models and agents are versioned, tested against the evaluation set and released with a way back.

Book an AI readiness assessment

How we work

From assessment to production, and beyond.

One partner across the whole life of your AI system, on AWS, Azure or Google Cloud. Each step has a clear output before the next one starts.

Book an AI readiness assessment
  1. Stage 01 · Find the use case worth proving

    Assess

    A short, fixed-scope assessment of your ideas, data, cloud and the models that fit.

    What happens
    1. 1.1Interviews with business and technical leads
    2. 1.2Candidate use cases scored on value, effort, data and risk
    3. 1.3Data, cloud and security readiness scored
    4. 1.4Models tested head to head on your data
    5. 1.5A phased roadmap and business case
    You get
    • A scored use case list
    • A readiness score
    • A model evaluation
    • A roadmap
    Your part

    Time with the people who own the problem and the data.

    Your safety net

    Nothing is built until you’ve agreed the first use case.

  2. Stage 02 · Prove value on one process

    Pilot

    We build the first use case on real data, against a metric agreed up front.

    What happens
    1. 2.1A pilot charter: scope, success metric and guardrails
    2. 2.2An evaluation set of real questions with known answers
    3. 2.3A working system on real data, in your cloud
    4. 2.4Guardrails and access controls from the start
    5. 2.5Results measured against the metric
    You get
    • A working pilot
    • Measured results
    Your part

    Give access to data and a few real users.

    Your safety net

    The pilot runs alongside the current process until it’s proven.

  3. Stage 03 · Make it safe to scale

    Build for production

    We harden the pilot into a system you can rely on.

    What happens
    1. 3.1Security review: access, secrets, private networking
    2. 3.2Release pipeline for prompts, models and agents
    3. 3.3Monitoring for quality, cost and usage
    4. 3.4Load and failure testing
    5. 3.5Runbooks and documentation
    You get
    • A production-ready system
    • Dashboards and alerts
    Your part

    Approve the security design and go-live plan.

    Your safety net

    Every release is tested against the evaluation set and can be rolled back.

  4. Stage 04 · Roll out in steps

    Launch and scale

    We roll out to more users and processes, one wave at a time.

    What happens
    1. 4.1Staged rollout to more users
    2. 4.2The same foundation reused for the next use case
    3. 4.3User feedback captured and acted on
    4. 4.4Cost and quality checked at each wave
    5. 4.5Training for the people who use it
    You get
    • More users, more value
    • A reusable AI foundation
    Your part

    Choose the next process to automate.

    Your safety net

    Each wave can be paused or rolled back.

  5. Stage 05 · Keep it getting better

    Operate and improve

    AI drifts without care. We keep watching it and improving it.

    What happens
    1. 5.1Quality, cost and drift monitored
    2. 5.2Evaluation set grown from real questions
    3. 5.3Prompts and models updated and re-tested
    4. 5.4Monthly review with you
    5. 5.5Support for incidents and changes
    You get
    • A monthly AI report
    • A system that improves
    Your part

    Join the monthly review and share feedback.

    Your safety net

    Everything is documented, so your team can run it too.

Our platform: QueryDash

Ask your data a question. Get an investigated answer.

QueryDash is our agentic investigation platform on AWS. Users ask in plain language; the agent plans, writes and checks read-only SQL, analyses the results and explains what it found, with follow-up questions. Four clients use it today.

Chatwit · Sales analytics

“Investigate why sales performance changed and guide me through the findings.”

  • Plans and runs several queries
  • Explains the drivers it finds
  • Keeps context for follow-ups

TNBT Tech · Operations

“Identify unusual operational changes and determine their likely causes.”

  • Finds the unusual changes
  • Suggests likely causes
  • Shows the evidence

Dexlyn · Business intelligence

“Determine the main factors affecting quarterly revenue.”

  • Multi-step analysis
  • Evidence for each finding
  • No SQL needed

Trueestate · Real estate

“Investigate why property bookings declined.”

  • Explores the booking data
  • Ranks the likely reasons
  • Answers follow-ups

Safe by design. Every SQL statement is checked before it runs, and anything that would change data is rejected. Built on Amazon Bedrock with Amazon Nova Pro, ECS Fargate, Aurora PostgreSQL and DynamoDB.

See QueryDash on your data

Proof

AI our clients use every day.

Stories from clients who agreed to be named. Every detail is from the original case study.

Ask about an AI build like yours
  • 8client AI solutions on Amazon Bedrock
  • 854products searchable for EasyGift
  • US$90–130a month to run EasyGift’s platform
  • 4clients on QueryDash
AWS
Arham Technosoft (EasyGift) · Corporate gifting

A request becomes an on-budget quote in minutes

Spreadsheet cataloguesSearch by words or photo

  • Products854, from 12 vendors
  • BudgetsHonoured exactly
  • Running costAbout US$90–130 a month
Read the story
AWS
Spotem · Corporate gifting

Upload a photo, get similar products in seconds

Comparing images by handVisual product search

  • SearchBy photo
  • ResultsWithin seconds
  • InfrastructureServerless, in code
Read the story
AWS
The One Technologies · Corporate gifting

Customer requirements become branded quotes

Quotes built by handAI quote automation

  • QuotesWithin minutes
  • ProposalsConsistent, on brand
  • AI serversNone to run
Read the story

Full stories, including HK Infosoft’s plain-language product search and the four QueryDash clients, are on our Resources page.

AWS Advanced Tier Services Partner badge

AWS Advanced Tier Services PartnerAlso: AWS Partner with SMB Competency

Microsoft Solutions Partner for Cloud and AI Platforms badge

Microsoft Solutions Partner for Cloud and AI PlatformsAlso: Infrastructure (Azure)

Google Cloud Partner badge

Google Cloud Partner

Why DevOps TechLab

AI engineers who ship to production.

One team for use case, data, AI build, security and the cloud underneath.

Meet an AI engineer
What it often looks likeWith DevOps TechLab
Starting pointA model demoA scored use case and a tested model
Where it runsA third-party SaaS toolYour own cloud account, your data
Model choiceWhatever is newestTested on your data for accuracy, speed and cost
ProofA good-looking demoA metric agreed up front and an evaluation set
SafetyReviewed at the endGuardrails and limits from the first sprint
Running costFound out on the billPlanned per request, and watched
CloudsOne AI platformAmazon Bedrock, Azure OpenAI or Vertex AI
After launchHanded over and leftMonitored and improved, or handed over with runbooks

Where this fits

One stage of the journey. Here’s what comes next.

AI needs a secure, well-run cloud underneath it, and an eye on what it costs.

  1. 01
    You are here

    Generative AI & data

    AI assistants, search and agents built on your own data.

    This page

  2. 02
    Next

    Cloud security & compliance

    AI governance, guardrails and evidence for audits.

    Explore

  3. 03
    Next

    Managed cloud services

    Day-to-day running of the cloud your AI runs on.

    Explore

  4. 04
    Next

    Cost optimisation (FinOps)

    Keep model and GPU spend visible and in check.

    Explore

FAQ

Generative AI and data, answered.

Straight answers on where to start, data, models, safety and cost. If yours isn’t here, ask us.

Ask a question

With an AI readiness assessment: we score your use case ideas, check your data and cloud, test the models that fit and give you a phased plan.

It’s short and fixed in scope, usually a matter of weeks. We agree the scope and dates with you before it starts.

Search by meaning or photo, assistants and chatbots, document processing, quote and proposal automation, and agents that investigate data and complete multi-step work.

AI readiness assessment

Get a scored roadmap, not another slide deck.

An AI engineer works with your team to score your use case ideas, check your data and cloud, test the models that fit and give you a phased plan. No obligation to build with us.

  1. 01Use cases, scoredValue, effort, data and risk for each idea.
  2. 02Readiness, scoredCloud, data, security and team.
  3. 03Models, testedAccuracy, speed and cost on your own data.
  4. 04A phased roadmapWhat to build first, the business case and next steps.