Artificial Intelligence

AI That Ships to Production, Not Just to Slides.

Data foundations solid enough to trust, with agentic workflows, grounded LLM applications and production ML engineered on top.

Explore AI Transformation
Key Challenges

Where AI & Data Initiatives Stall Before They Scale

Data Not ReadyModels starved by messy dataWithout pipelines and governance, every AI initiative starts from zero.
Endless POCsDemos that never reach usersImpressive prototypes die without the engineering to productionize them.
Hallucination RiskOutputs you can't put in front of customersUngrounded LLMs are a liability in regulated, customer facing workflows.
Unclear ROISpend without measurable outcomesAI budgets evaporate when use cases aren't tied to business metrics.
What we deliver

AI & Data Innovation Services

Embed intelligence into the products and workflows that move your business.

Data Engineering & PlatformsThe foundation AI depends on: lakehouse architectures, streaming pipelines, and governance that makes data usable.
◆Lakehouse architecture on Databricks or Snowflake
◆Batch and streaming pipelines with lineage built in
◆Governance and access control before modelling
◆Migration off brittle ETL without pausing reporting
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Machine Learning EngineeringProduction ML pipelines, feature stores, training infrastructure, and MLOps that keep models honest over time.
◆Reproducible training and inference pipelines
◆Feature stores aligning training and serving
◆CI/CD for models with drift monitoring
◆Serving infrastructure sized to real traffic
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Generative AI & LLMsRAG applications and fine tuned models grounded in your data, accurate, auditable, and safe to ship.
◆Retrieval grounding with checkable citations
◆Evaluation suites and golden datasets
◆Fine tuning where it beats retrieval alone
◆Guardrails and audit logging for regulated work
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Agentic AI SolutionsAutonomous agents that execute real workflows, with tool use, guardrails, and human in the loop controls built in.
◆Agents wired to your systems via typed tools
◆Human in the loop approval on high stakes actions
◆Deterministic orchestration and retries
◆Full traceability of every step and why
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AI Product DevelopmentFull products with AI at the core, from copilots to intelligent automation, designed, built, and operated.
◆Design and engineering around the AI, not bolted on
◆Copilots embedded in existing workflows
◆Automation that removes manual steps end to end
◆Operated after launch: evaluation and cost control
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AI Workshops & EnablementExecutive strategy sessions and hands-on team enablement, an AI roadmap tied to business metrics, not hype.
◆Executive sessions that cut through vendor noise
◆Scoring by business value and feasibility
◆Hands-on enablement for your engineers
◆A sequenced roadmap with a metric per phase
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Process

Our AI Delivery Steps

01Use Case & Readiness AssessmentWe score candidate use cases by value and feasibility, and audit the data that will feed them, in weeks, not quarters.
02Grounded PrototypeA working prototype on your real data with evaluation baselines, accuracy measured before anything is promised.
03ProductionizeGuardrails, observability, cost controls, and CI for prompts and models, AI engineered like software, because it is.
04Operate & ImproveContinuous evaluation, drift monitoring, and a metrics dashboard tying model performance to business outcomes.
Proof of Concept

Prove it on your data before you scale it.

Each is a working slice built on your own data and evaluated against your own accuracy and compliance bar, so you see real output before committing to a full build.

Grounded assistantOne customer or internal workflow answered from your own documents, scored for hallucination and citation accuracy.Typical timeline: 3-4 weeks
Predictive modelA single forecast or scoring model trained on your historical data and measured against the decision it would replace.Typical timeline: 4-6 weeks
Data platform sliceOne pipeline built end to end, proving quality, lineage, and refresh rate hold before the wider platform is committed.Typical timeline: 3-5 weeks
Document automationOne high volume document type extracted and validated, with accuracy and exception rates measured against manual handling.Typical timeline: 2-4 weeks
Scope a proof of concept
Tools & Technologies

Equipped with the latest tools

ClaudeOpenAI (GPT)LangChainLangGraphPyTorchTensorFlowHugging FaceVertex AIAWS BedrockAzure AI FoundryPineconepgvectorMilvusDatabricks (Mosaic AI)MLflowWeights & BiasesRayKubernetes
Industries we serve

Helping every industry put AI to work

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Travel & HospitalityBooking, OTA reconciliation and support automation for platforms handling high transaction volume.
Retail & CPGDemand forecasting and personalization agents that turn transaction and inventory data into action.
Oil, Gas, and EnergyPredictive maintenance and operational data pipelines for safety critical, asset heavy environments.
Banking & FinanceFraud detection and document intelligence grounded in regulated, auditable data.
Engagement Models

Three ways to bring us in

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Project basedFull AI BuildThe team behind your AI system end to end: discovery, architecture, model development and MLOps.Best for a defined outcome
OngoingEmbedded AI TeamAI engineers and ML specialists working inside your team and your sprints, for production velocity.Best for continuous delivery
Fixed scopeAI Readiness AssessmentA short engagement scoring your data, infrastructure, and use cases, returning a costed plan.Best for deciding what to do
FAQs

Frequently Asked Questions

Still have a question about data readiness, accuracy, or where to start?
Let's talk
What does "AI & Data Systems" mean at Devsinc?+
A dedicated team that builds the data foundations and the AI systems that run on them, not just a model bolted onto whatever data you already have.
Do you build the data platform too, or only the AI layer on top?+
Both, usually in that order. Most AI initiatives fail because the data underneath isn't ready, so we build or shore up the pipelines first.
Our data is a mess. Can we still do AI?+
Usually yes. Most use cases need a thin, well governed slice of data, not a finished lakehouse. We build the pipeline for the use case and grow it from there.
How do you prevent hallucinations in production?+
Retrieval grounding, structured outputs, evaluation suites with golden datasets, and human in the loop for high stakes actions.
Build or buy, when should we build?+
Buy for commodity capabilities, build where AI touches your differentiation. The readiness assessment gives you that map.
How do you keep our data private?+
Private deployments (VPC or on premise), zero retention API agreements, PII redaction in pipelines and audit logging, compliant with SOC 2, HIPAA and GDPR requirements.
What does an engagement cost and how fast is value visible?+
A readiness assessment runs 2-3 weeks. First production use case typically ships within a quarter, with its business metric instrumented from day one.
Get started

Ready to get your AI out of pilot purgatory?

Tell us your use case and your data, we'll map the fastest route to production.
✓Bring your use cases, your data and your compliance requirements
✓One free readiness assessment mapping the fastest route to production
✓Your business metric instrumented from day one

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