PROFESSIONAL SERVICES

The last mile, delivered by our engineers.

Enterprise AI is never plug-and-play — internal system integration, data governance, compliance, fine-tuning all need a professional team to close the loop. We run an FDE model, so the promise meets production.

Why enterprise AI is never plug-and-play

The real obstacles to enterprise AI adoption have never been model capability. They are legacy ERP interfaces, extremely specific internal network gateways, 2-day cross-team approvals, and data that cannot touch external models. Lakesis treats this last mile as part of the product — not vendor handover, but our engineers walking the path with you.

01
FDE · FORWARD DEPLOYED ENGINEERING

Forward Deployed Engineers (FDE)

Lakesis engineers sit inside your business and adapt our standard products to your unique workflows. The Palantir model — not vendor-handover, but engineers and business owners at the same table.

  • On-site or remote, depending on compliance and pace
  • Pod composition: platform engineer + solution architect + data engineer
  • Rapid PoC (2–4 weeks) → pilot (4–8 weeks) → scale-up
  • In-context prompt / workflow / data governance design
02
SYSTEM INTEGRATION

Integration & custom development

Legacy ERP, special internal networks, custom OA, in-house APIs — Lakesis integrates all of them. 90% of "AI not working" is actually integration, and we own the engineering for it.

  • ERP: SAP / Yonyou / Kingdee / Oracle / in-house
  • Collab: Lark / WeCom / SharePoint / Notion / Confluence
  • Data sources: RDS / MaxCompute / ADS / Postgres / MongoDB
  • Auth: SSO / OAuth / enterprise IdP / LDAP / AD
03
PRIVATE · HYBRID · CLOUD

Deployment & operations

Pick the deployment shape that fits your compliance and data sovereignty — on-prem GPU cluster, internal K8s, dedicated cloud, or public. Lakesis handles architecture and rollout, then SLA-grade ops.

  • Private: customer-internal GPU cluster / K8s / single host
  • Dedicated cloud: Alibaba / Tencent / Huawei dedicated instances
  • Hybrid: inference in private VPC, control plane in cloud
  • SLA 99.9% · monitoring + alerts + monthly review
04
MODEL · KNOWLEDGE · PROMPT

Fine-tuning & continuous tuning

Generic models hit a 30% gap in enterprise context. We fine-tune on private data, build domain prompt libraries, design knowledge-graph ontologies — so the model actually speaks your dialect.

  • Domain corpus fine-tuning (LoRA / full · Claude / GPT / domestic)
  • Domain prompt library + system message engineering
  • Ontology design + knowledge graph construction
  • Runtime evaluation + regression + continuous iteration
05
ARCHITECTURE CONSULTING

Architecture & strategy consulting

Before writing code, clarify the roadmap. We use an L1→L4 maturity framework to diagnose — should you start with data governance, or jump straight to agents?

  • AI maturity assessment (data · process · org)
  • Roadmap design (3 / 6 / 12 month)
  • Security / compliance / data sovereignty architecture review
  • Procurement & SOW templates to accelerate internal approval
NEXT STEP

Start with an architecture diagnosis.

Whether you are exploring, running a PoC, or scaling — one in-depth conversation, we'll give you an AI adoption path.

Book an architecture diagnosis See customer cases