Prepare
Parse, segment, normalize, redact, and assemble the approved context for the task.
Compass · AI processing and orchestration
A private operating layer for applied AI.
Compass connects approved information, models, tools, and persistent context in one controlled runtime. Use it to classify, research, compare, enrich, and synthesize work with local models, approved provider APIs, or both.
Documents & dataAPIs & feedsWorking context
Compass runtimePrepare · Direct · Work · Return
Policies, tools, models, attribution, validation, and run history.
Structured dataResearch packagesAlerts & actions
From input to usable output
More than a model connection.
Compass prepares information, directs each task to the right model or tool, preserves attributable work, and returns a result that another system or person can use.
Direct
Select models, tools, prompts, policies, and approval points for the workflow.
Work
Run focused agents, parallel model review, tool calls, retries, and validation.
Return
Deliver structured data, research packages, alerts, briefs, or application-ready output.
Compass can operate independently, sit behind an existing application, or add AI processing to Meridian and other Lambspring systems.
Model choice
Use the model that fits the work.
Deployment does not have to be all-local or all-cloud. The processing path can change by task, sensitivity, capability, cost, and available hardware.
Keep suitable workloads close.
Run compatible models inside the environment. Available model size, speed, and throughput depend on the hardware assigned to the work.
Connect approved capabilities.
Use compatible frontier and specialist providers through their available APIs when the task benefits from their capability, knowledge, or scale.
Route deliberately.
Screen, extract, or prepare locally, then send only selected work to an external provider under the workflow's defined boundary.
Agentic operation
Agentic where useful. Bounded by design.
Compass can search approved sources, call tools, retain working context, compare independent model views, test a result, and return a finished package rather than a loose chat transcript.
Tool access, credentials, model choices, schedules, spending limits, and approval points are defined for the workflow.
Tools and APIs
Connect approved applications, databases, search surfaces, and specialized services.
Persistent context
Carry forward useful working state and source references across recurring work.
Multi-model review
Compare independent views without erasing attribution, disagreement, or uncertainty.
Scheduled execution
Run on a clock, an event, a queue, or an explicit human request.
Structured output
Return validated fields, packages, files, notifications, or application-ready results.
Controls and history
Retain permissions, approval state, model identity, retries, and operating evidence.
Deployment
Deploy where the data belongs.
The environment can sit on customer premises, inside customer-controlled cloud infrastructure, or in a dedicated Lambspring-managed deployment.
Customer premises
A private local or on-premises installation sized around available compute, security requirements, and expected workloads.
Customer cloud
A dedicated deployment inside customer-controlled cloud, identity, networking, and data boundaries.
Lambspring-managed
A dedicated environment operated on Lambspring hardware or in suitable cloud infrastructure, with ongoing support available.
Operational boundaries stay explicit.
- Source quality and provenance still matter.
- Model output remains probabilistic.
- Local performance depends on assigned hardware.
- Provider availability and policy remain provider-controlled.
- Consequential actions can retain human approval.
- Specific integrations are confirmed during deployment.
Start with one useful workflow
Bring the information, tools, constraints, and result you need.
Lambspring Labs will map the processing path, deployment boundary, and appropriate mix of local and connected models.