Artificial Intelligence

AI That Ships to Production, Not Just to Slides.

Explore AI Transformation
What we deliver

AI & Data Innovation Services

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

Sources
CRMapiEventsstreamERPbatch
Pipeline
normalize✓dedupe
Freshness4 min
Lakehouse
bronzesilvergold
lineage tracked
96% coverage
Data Engineering & PlatformsThe foundation AI depends on: lakehouse architectures, streaming pipelines, and governance that makes data usable.Talk to this team
TrainServeMonitorDriftwithin SLO
Machine Learning EngineeringProduction ML pipelines, feature stores, training infrastructure, and MLOps that keep models honest over time.Talk to this team
QueryWhich policy covers refunds after 30 days?
Retrieved from knowledge baseRelevance   Score
refund-policy.md0.91
terms-v4.pdf0.84
support-faq0.62
12 chunks scanned3 above threshold
Grounded answer
3 sources citedgroundedness
Generative AI & LLMsRAG applications and fine tuned models grounded in your data, accurate, auditable, and safe to ship.Talk to this team
Agentplan / actapproval
search_orders()issue_refund()update_crm()
plan: 3 stepstool: search_ordersgate: approval held
Agentic AI SolutionsAutonomous agents that execute real workflows, with tool use, guardrails, and human in the loop controls built in.Talk to this team
Tickets resolved
1,284
Live
AI suggested: merge 3 duplicate tickets
TicketStatus
Refund not processedOpen
Duplicate: order #4821AI flagged
Address change requestResolved
AI Product DevelopmentFull products with AI at the core, from copilots to intelligent automation, designed, built, and operated.Talk to this team
Use caseValue    Feasibility    Score
Support ticket triage8.6
Demand forecasting6.4
Contract summarization5.1
9 use cases scored3 shortlisted for phase 1
2-week sprintAssessuse cases scoredRoadmaproadmap sequencedEnableteam enabled
AI Workshops & EnablementExecutive strategy sessions and hands-on team enablement, an AI roadmap tied to business metrics, not hype.Talk to this team
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 BedrockClaudeOpenAI (GPT)LangChainLangGraphPyTorchTensorFlowHugging FaceVertex AIAWS Bedrock
Azure AI FoundryPineconepgvectorMilvusDatabricks (Mosaic AI)MLflowWeights & BiasesRayKubernetesAzure AI FoundryPineconepgvectorMilvusDatabricks (Mosaic AI)MLflowWeights & BiasesRayKubernetes
Engagement Models

Three ways to bring us in

Let's Talk
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?

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