Lakemind Studio
Design agents
against real systems
Analysts describe the outcome; the platform handles tools, memory and handoffs.
Agentic AI · Data platforms · Delivery
Enterprise AI that runs on your data, not someone else's.
Logilake builds and runs the agents, platforms and data foundations that put AI to work inside regulated enterprises — then stays on to operate them.
Logilake works in three layers: Surface, agents and AI products; Mid Water, the agentic data platform; and Lakebed, the Lakeworks delivery factory.
Delivery centers
Mumbai, Pune, IndiaEngineering Hub
Dubai, UAEClient Hub
Runs in your cloud, on-prem or air-gapped. Nothing leaves the perimeter.
The Lakemind platform
The Lakemind platform — wherever your data lives
Design agents
Surface
Agents & AI
Products
Copilots, autonomous agents and decision products your teams actually use.
Mid Water
Agentic Data
Platform
Governed, semantic data — modelled as an ontology, not just tables.
Lakebed
Lakeworks
delivery factory
Human-orchestrated, agent-executed delivery, plus 24×7 run support.
The Lakemind platform
One platform to build, run and prove enterprise agents.
Model-agnostic and deployable in your cloud, on-premise or air-gapped. No lock-in, no data leaving your perimeter. The platform wraps all three segments — Surface, Mid Water and Lakebed.
Lakemind Studio
Design agents and multi-agent teams against real enterprise systems. Business analysts describe the outcome; the platform handles tools, memory and handoffs.
- Visual agent builder — roles, tools, guardrails, escalation paths
- MCP tool registry — connect SAP, ServiceNow, Salesforce, internal APIs
- Human-in-the-loop — approval gates on any action that writes
- Governance — Control access, policies, data processing , usage & cost
Lakemind Observe
Evaluation, monitoring and audit. Prove the agent is right before it ships, and prove it stayed right after.
- Golden-set evals — regression tests for prompts, tools and models
- Drift & cost alerts — per agent, per team, per token
- Immutable audit trail — every action, tool call and approval, replayable
Agentic AI · Data platforms · Delivery
From data lake to decision — in one lineage
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Surface · what the business touches
Agents & AI Products
Copilots, autonomous agents and decision products your teams actually use.
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Mid Water · what AI feeds on
Agentic Data Platform
Governed, semantic, machine-readable data — modelled as an ontology, not just tables.
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Lakebed · what keeps it running
Lakeworks delivery factory New
Human-orchestrated, agent-executed delivery. Digital workers do the build; our architects own the decisions and the exceptions — plus implementation, app development and 24×7 run support.
Partnered with
- AWS
- Azure
- Databricks
- Snowflake
- Salesforce
- Qlik
- Automation Anywhere
Layer 01 / 03
Surface
What the business touches
Agents & AI Products
Surface · Agent Library
Surface.
Agents that arrive already knowing your process.
Not generic copilots. Pre-built agents that understand a specific business process or a specific industry — shipped with the data model, the controls and the exception paths already in place. Deployed on Lakemind, grounded on your business ontology, live in weeks.
Surface · Process Agents
Process agents
End-to-end ownership of a workflow, not a step inside it. Each agent reads, decides, acts in the system of record, and escalates what it should not decide alone.
Procure-to-Pay
Requisition to payment: validates the PO, matches the receipt, posts the entry.
- Three-way match with tolerances
- Supplier screening & master data
- Touchless posting to SAP or Oracle
Invoice & Exceptions
Reads any invoice, finds why it failed, and fixes what it can before a human sees it.
- PDF, EDI, email and scanned paper
- Mismatch diagnosis vs PO and contract
- Duplicate and fraud detection
Pricing & Decisioning
Optimises price and margin in real time, routing exceptions to the right approval path.
- Price optimisation against live commercial data
- Margin and discount policy enforcement
- Quote and exception approval routing
Order-to-Cash
Clears the credit check, chases the receivable, reconciles the cash.
- Order validation & credit risk
- Dunning tuned to account tier
- Cash application & dispute triage
Supply Chain Verification
Checks that what was promised is what arrived, before the shipment moves.
- Supplier document verification
- Batch and lot traceability
- Sanctions & customs screening
Your process
Every agent is built on the same Lakemind primitives and the same ontology. A process that is not on this list is a configuration exercise, not a new engineering programme — usually six weeks.
Surface · Domain Agents
Domain agents
Trained on the vocabulary, systems and regulations of one industry. They arrive knowing what a planogram, an FNOL or an OEE loss actually is — and the ontology behind them is modelled for that sector before the first agent runs.
Retail & CPG
Retail Agents
Sense demand, protect margin and keep the shelf right, store by store.
- Demand sensing & replenishment
- Markdown & price optimisation
- Planogram compliance
- Store operations copilot
Manufacturing
Manufacturing Agents
Sit between the shop floor and the ERP, turning telemetry into a decided action.
- Downtime root-cause analysis
- Quality inspection triage
- Spares & MRO planning
- Maintenance work-order drafting
Banking & capital markets
Finance Agents
Built for work that has to survive an audit, with every decision cited.
- KYC refresh & periodic review
- AML alert triage
- Credit memo drafting
- Regulatory reporting checks
Insurance
Insurance Agents
From first notice of loss to settlement, with the policy read in full every time.
- FNOL intake & claims triage
- Coverage & policy interpretation
- Subrogation identification
- Underwriting submission review
Healthcare & life sciences
Healthcare Agents
Documentation and case handling under clinical and regulatory constraint.
- Prior authorisation
- Clinical document summarisation
- Pharmacovigilance case intake
- Claims & coding review
Logistics
Logistics Agents
Keep freight moving and paperwork clean across carriers and borders.
- Freight invoice audit
- ETA exception management
- Customs documentation
- Carrier performance review
Your process isn't on this list? Every agent here was built in Lakemind Studio on the same primitives. Yours can be too — usually in six weeks.
Bring us a workflowLayer 02 / 03
Mid Water
What AI feeds on
Agentic Data Platform
Mid Water · Agentic Data Platform
Mid Water.
Agents cannot act on data that has no meaning.
Most AI programmes stall on the data, not the model. Retrieval tells an agent what a document says; an ontology tells it what the business is. These products are how we get there — and they ship as products, not slideware.
Mid Water · Meaning & Autonomy
Ontology & autonomous data products
One gives agents a model of the business; the other lets them build and run data products against it. Together they turn a data estate into something an agent can act on safely.
Ontology modelling
LakeGraph
Models the enterprise as entities, relationships and rules rather than tables and joins. Agents reason over a business ontology, so ‘active customer’, ‘open claim’ or ‘qualified supplier’ means exactly one thing across every agent, report and API.
- Ontology designer — entities, attributes, relationships and business rules
- Knowledge graph built from existing schemas, documents and lineage
- Mapping layer — physical tables bound to ontology concepts, versioned
- Graph retrieval surfaced directly to every agent
Components
Ontology DesignerKnowledge Graph BuilderInference EngineMapping LayerGraph Retrieval API
Autonomous data products
LakeMint
Agents design, build, test, document and publish governed data products end to end — then keep them healthy. Contracts, quality rules, documentation and APIs are generated against the ontology rather than hand-written and left to rot.
- Agent-authored data products from an ontology concept and an intent
- Data contracts and quality rules generated, versioned and enforced
- Self-documenting — definitions, lineage and ownership always current
- Published as governed APIs to the data product marketplace
Components
Product Authoring AgentContract GeneratorSelf-Heal AgentData Marketplace APILineage Publisher
Agentic data engineering
LakeFabric
Ingestion, quality, transformation and governance driven by configuration and agents rather than hand-written pipelines. Data products publish themselves as governed APIs.
- Zero-code orchestration across cloud and on-premise
- Autonomous data-quality and reconciliation agents
- Domain-aligned modelling — data mesh without the theory
Solutions
Pipeline AutogenDQ Agent PackDomain Mesh Builder
Modernization framework
LakeShift
Legacy warehouse to lakehouse, with the pipelines, tests and consumption layer translated by agents and validated row-for-row before cutover.
- Automated code conversion — Teradata, Informatica, SSIS, Oracle
- Data Authorization parity testing before a single user moves
- Typical outcome: 1,000+ pipelines , consumption layer artefacts in weeks, not months
Frameworks
Legacy → Modern Consumption LayerQlik → DatabricksOracle → SnowflakeSQL Server → DatabricksInformatica → Talend
Self Service & Conversational Analytics
LakeForge
Reframes data and consumption layer you already have into something both people and agents can consume: semantic, versioned, headless and conversational.
- Semantic layer and metric definitions — one set of numbers
- Headless BI — metrics served as APIs to agents, apps and dashboards
- Conversational analytics across the data, semantic and consumption layers
- Self-service analytics enablement
Solutions
LakelenseDatalenseSemantic Layer creatorHeadless BI Gateway
Trust & governance
LakeGuard
Granular access, lineage and policy enforced at the business-function level — the control plane that makes agents safe to let loose.
- ScanIQ — One platform for discovery , governance and rationalisation
- GraphQ- Knowledge graph of your Data estate
- Audit Q - Audit trails aligned to DPDP, GDPR and ISO 27001
- Deploy Q- Agentic framework Policy and deployment checks enforced before release
Solutions
ScanIQGraphQAuditQDeploy Q
Mid Water · The numbers
Grounded everywhere
- 67%Faster migration timelines with LakeShift accelerators
- 4×Smaller engineering teams needed per delivery
- 62%Invoices processed touchless within two quarters of rollout
- 90dFrom first working session to an agent in production
Logilake models your business as an ontology, puts agents to work on the processes that run on it, and escalates what they shouldn't decide alone.
It deploys in your cloud, on-premise or air-gapped, and lives inside the systems you already run.
Mid Water · Integrations
Plugs into
what you run
SAP, ServiceNow, Databricks, Snowflake — new connectors land every week.
Browse integrationsLayer 03 / 03
Lakebed
What keeps it running
Lakeworks delivery factory
Lakebed · Delivery
Lakebed.
Where it all gets built: agent-executed engineering, with architects owning every decision.
Someone still has to build it , so Two ways we deliver. Lakeworks industrializes the repeatable work into an agent factory. Lakecraft is the human practice behind it — the consulting, engineering and run services Logilake has always provided, now pointed at AI-era systems and covered by the same SLAs.
Lakebed · Lakeworks — Agentic Delivery Factory
Agentic delivery factory New
Lakeworks
An industrialized delivery model where digital workers execute and human specialists orchestrate. Every engagement runs through the same governed factory — so quality stops depending on who happens to be staffed on it.
Human-orchestrated. Architects own the decisions and the exceptions.
Agent-executed. Discovery, code conversion, testing and monitoring run as agents.
Assured. Every artifact passes a review gate before it moves downstream.
Repeatable. Reusable patterns, not a fresh opinion per project.
Our agents, your estate
We deploy pre-built Lakeworks agents into your environment. No agent development effort, fastest time to value, enterprise learnings already baked in.
Built together, inside your stack
We co-design and build agents within your cloud and data platform. Joint ownership, made for strict data-residency and regulatory constraints.
Your agents, our assurance
You bring your own agents and IP. We act as orchestrator, integrator and quality layer — lifecycle management, AI code review, optimization at scale.
Lakeworks · Human-orchestrated, agent-executed
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Business Analysis
Analyst AgentRequirement gathering & BRDs from source systems
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Design
Design AgentData layer & medallion architecture
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Build
Builder AgentBI dev & data modellingAutoBuilder · Data Modeller
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Validate
Validation AgentRow-level parity & defect triage
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Deploy
Support AgentRelease management & environment promotion
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Operate
RCA & Pipeline AgentFailure analysis, fix & re-run
Human in the loop at every gate — review, approve, escalate
Lakebed · Lakecraft — The Human Practice
Lakecraft
The human practice
Where Lakeworks industrializes the repeatable, Lakecraft handles the work that still needs judgement — senior people on your problem, under contract, for as long as it takes.
Managed Services
L1–L3 application, platform and agent support. 24×7, follow-the-sun, with incident and change management built in.
Implementation Services
Platform rollouts on Databricks, Snowflake, Azure and AWS — architecture through adoption.
AI Strategy & Advisory
Use-case portfolio, value case, target architecture and the operating model to sustain it.
Application Development
Custom products and internal tools, built AI-native from the first commit rather than retrofitted later.
Staffing & Extended Teams
Vetted data and AI engineers embedded in your squads, with our knowledge base behind them.
Enablement & Training
Curated programmes for engineers, analysts and leadership so adoption outlives the project.
How we engage
Start with one workflow. Earn the next one.
A sequence, not a transformation programme. Each stage has an exit criterion, and you can stop at any of them.
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Stage 01
Working session
Two days with your team. We leave with a ranked use-case list and an honest read on data readiness.
Week 0 -
Stage 02
Grounded pilot
One agent, real data, measured against a baseline you set. No synthetic demos.
Weeks 1–6 -
Stage 03
Production
Guardrails, evals, audit and integration. It goes live with an owner and an SLA.
Weeks 7–12 -
Stage 04
Scale & run
Next workflows onto the same platform and ontology. We operate it, or hand it to your team.
Quarter 2+
Let's start
Bring one workflow. We'll show you what it costs to fix it.
Book a two-day working session with our architects. You'll leave with a ranked use-case list, an ontology sketch of the domain, and a data-readiness assessment — whether or not you work with us.