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Where the sands end,AI lands.

We embed engineers inside your enterprise and connect scattered data, processes and models into AI systems that genuinely take part in judgment and execution — starting from one business problem worth solving, delivered on real data, and accountable for the outcome.

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Three practices
4–8wk
From locked use case to running prototype
100%
AI conclusions traceable to source data
0
Data leaving your network in private deployments
Thesis

We don't sell software. We deliver one specific business problem, actually solved.

01

Find the use case worth money

Start from the three most expensive wrong decisions you make every day, put a price on the pain, then decide whether to build.

02

Ship a working system on real data

No slideware. With your data and your processes, we build something with visible results in weeks.

03

Then talk platform and scale

Once one use case runs and the numbers hold up, we replicate it across more departments, plants and business lines.

Practices · 三大业务

From strategy to models,
one team, end to end

Forward deployed engineering decides what to build and how it lands; agent systems let AI actually execute; model engineering turns capability into assets the enterprise owns. Engage any one on its own, or combine all three into a complete AI transformation.

01
前线部署工程
Enterprise AI transformation

Forward Deployed Engineering

Engineers on site — not a strategy deck.

Our forward deployed engineers (FDEs) act as engineer, product manager and solutions architect at once, working side by side with your business and technology leads. We find the decisions that genuinely lose money every year, connect data across ERP, MES, WMS, CRM and other systems, build a business object model AI can understand, then embed AI into everyday workflows.

On-site deliveryBusiness × tech in one teamNo middle layer
FIG. 01ERPMESWMSSRMCRMPLMIoTBIONTOLOGYObject modelFDE
Deliverables
  • 01AI maturity and value-case assessment
  • 02Use-case map and ROI model
  • 03Data integration and business object modeling (Ontology)
  • 04AI decision workflows embedded in daily operations
  • 05Enablement and path-to-production roadmap
02
智能体系统
Lightweight AI agent development

Agent Systems

Not chatbots — digital workers that read data, judge, call tools and act.

We build enterprise-grade agents fast on a lightweight architecture: understand the task, read the data, make a judgment, call tools, take action, observe the result, and judge again. We reuse your existing systems and permission model instead of starting over; every conclusion carries its evidence, and every critical action is approved by a person.

Delivered in weeksLightweight deploymentExplainable
FIG. 02
UnderstandRetrieveReasonTool CallApproveExecuteObserve
Step 01 · 理解
Understand
Parse task intent and constraints
Deliverables
  • 01Business agents and multi-agent orchestration
  • 02Enterprise knowledge base and retrieval augmentation (RAG)
  • 03Tool calling and integration: API · database · RPA
  • 04Human-in-the-loop approvals and audit logs
  • 05Evaluation framework and continuous iteration
03
模型工程
Deployment · fine-tuning · training data

Model Engineering

Turn models into assets your company owns.

We deploy open-weight LLMs and inference services in your data center or dedicated cloud, fine-tune and preference-align them on your business data, and build a training-data pipeline from collection, cleaning and labeling to synthesis and evaluation. Model weights, datasets and evaluation baselines all stay in your hands.

Data stays in-houseFully under your controlMeasurable cost
FIG. 03
weights · layer 24 / attn.q_proj● training
DATA
SFT
LoRA
DPO
EVAL
SERVE
LOSSSTEPS →
Deliverables
  • 01Private deployment and inference optimization: vLLM · quantization · multi-GPU scheduling
  • 02Model gateway, quotas and access control
  • 03Domain fine-tuning: SFT · LoRA · DPO
  • 04Training data governance: collection · cleaning · labeling · synthesis
  • 05Evaluation benchmarks and go-live acceptance
Operating Stack

One enterprise AI stack that turns data into action

The three practices aren't isolated products — they are facets of the same stack. Hover or tap a practice to see the layers it covers.

  • L6
    Action执行与结果

    Write back to business systems, create tasks, trigger actions — ultimately measured in ROI.

  • L5
    Workflow业务工作流

    Judgments embedded in approval and execution flows, with a human always in the loop.

  • L4
    Agents智能体

    Reason over business objects, call tools, and produce recommendations backed by evidence.

  • L3
    Models模型层

    Privately deployed LLMs and domain fine-tuned models, routed and metered through one model gateway.

  • L2
    Ontology业务对象模型

    Orders, suppliers, inventory and equipment, abstracted into objects and relationships AI can understand.

  • L1
    Data数据接入

    Read-only connections to ERP, MES, WMS, SRM, CRM and your data warehouse. Existing systems stay untouched.

FIG. 04 — Sandsend operating stack, exploded view
Live Scenario

AI that doesn't just answer — it makes things happen

Take supply-chain line-stoppage risk. The system links supplier, procurement, logistics, inventory and production-plan data to spot risk early, simulate the impact, propose a response and, once a person approves, create the task directly.

Illustrative scenario · demo data, not a real customer
Supply Chain Control Center
DEMO
Supplier risks
0
High-risk suppliers
0
SKUs at risk
0
7-day line-stop risks
0
Risk index · last 30 days— — Alert threshold
Supplier matrix · 48
Agent trace
DETECT
Engagement

Start small, fast and real. Then go big.

Every phase has a clear output and clear criteria for moving on. Acceptance isn't the system going live — it's the business actually running on it. That one we're willing to put in the contract.

场景验证

Validation

01
1–8 weeks

Build something that genuinely runs, on your real data.

  • Data diagnostics and process mapping
  • Business object modeling
  • Agent prototype and workflow
  • ROI model and production roadmap
生产部署

Production

02
3–6 months

Woven into daily operations and tied to performance metrics.

  • Production system and integrations
  • Permissions, audit and security
  • Model deployment and ongoing evaluation
  • Training and operations handover
平台扩展

Expansion

03
Annual

Replicate proven capabilities across more departments and sites.

  • More use cases and business lines
  • Group-wide platform and model gateway
  • Continuous iteration and co-building
  • Annual framework agreement
Trust

What large enterprises fear most is AI seeing data it shouldn't

That's why security, permissions and audit aren't add-ons — they're part of the system.

01

Private deployment

Delivered in your data center or dedicated cloud. Data never leaves your network.

02

Read-only access

No changes to existing systems; permissions follow your SSO and accounts.

03

Traceable conclusions

Every judgment points to specific documents, records and rules.

04

Human in the loop

AI proposes judgments and recommendations; critical actions run only after human approval.

05

Full audit trail

Every query, inference and action is logged — replayable and accountable.

06

You own the assets

Models, datasets, methods and documentation are all handed over to you.

Sectors

Deep in only a few industries

The more focused the industries, the more our methods, data models and experience compound. We prioritize large enterprises with many systems, scattered data, and critical decisions still run on experience and spreadsheets.

  • Automotive · New energy · Batteries · Semiconductors · Chemicals · Pharma
    Typical use cases
    Line-stoppage early warningInventory and slow-stock optimizationUnplanned equipment downtimeQuality issue root-cause tracing
  • Large-scale logistics · Cross-border · Warehousing & distribution
    Typical use cases
    Delivery delay predictionCapacity and route schedulingAutomated exception handlingSupplier performance scoring
  • Retail groups · E-commerce · Commodity trading
    Typical use cases
    Demand forecasting and replenishmentPurchase price optimizationPricing and promotion decisionsStore operations assistant
  • Banking · Insurance · Securities · Consumer finance
    Typical use cases
    Risk and compliance reviewResearch and report assistanceOperations process automationPrivate model foundation
Begin

What are the threecostliest wrong callsyour company makes daily?

Book a 30-minute use-case diagnostic. No slide decks, no rush to quote — we listen to where you are today, then tell you which problem is worth solving with AI.

Book a diagnostic
haven@sandsend.ai