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SLM-Works

Browse all offerings or jump to a detail page: Custom SLM, Infrastructure, Private LLM, Hybrid routing, Agent orchestration.

Services built for owned, production-grade AI

Move from generic pilots to private models you control - whether you need a distilled SLM, a larger private LLM, routing between them, or orchestration on top. Pick a lane below or follow the journey to see how engagements typically flow.

Typical buyer journey

Most engagements move Build through Extend: When you need a larger model inside your boundary first, the Private LLM path branches after Run and rejoins before broad Scale: Use Tab to explore each stage.

Buyer journey diagram. Main path: Build, then Run, then Scale, then Extend. Alternate dashed path: after Run, Private LLM, then rejoin before Scale.

Model design, data strategy, and training or adaptation scoped to your use case and compliance boundaries.

Hardened inference, monitoring, and first production traffic with clear rollback paths.

Capacity, cost controls, and platform patterns as adoption grows across teams or regions.

Workflows, routing, and orchestration so models stay useful inside real business processes.

Alternate path

When task breadth needs a larger private model first, we deploy and harden in your boundary, then compress or route as your roadmap matures.

All services

From model design to production infrastructure: five ways we take you from AI experiment to owned, private AI on your infrastructure.

  • Custom SLM development

    Design and train task-specific small language models, then compress them for latency and cost targets your workloads actually need. We align architecture with your data boundaries and evaluation gates - not a one-size-fits-all checkpoint export.

    Key deliverables

    • Use-case and dataset strategy workshops
    • Training, distillation, and quantization plans
    • Offline evaluation harnesses aligned to your KPIs
    • Handover artifacts for your ML and platform teams
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  • SLM infrastructure

    Stand up reliable inference, batch, and observability for compact models on hardware you operate - VPC, dedicated cloud, or colo. We stay pragmatic about GPUs, autoscaling, and cost controls so SLMs stay cheaper than raw API sprawl at volume.

    Key deliverables

    • Reference deployment patterns for your stack
    • Monitoring, logging, and capacity guidance
    • CI-friendly promotion paths for model versions
    • Runbooks tuned to your SRE practices
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  • Private LLM deployment

    When a larger private model is the right starting point, we deploy and secure it inside your perimeter - then plan compression or routing when the workload profile justifies it. No surprise data egress; access and logging follow your policies.

    Key deliverables

    • Sizing and residency-aware architecture
    • Hardened serving and access controls
    • Integration with existing identity and audit tooling
    • Roadmap toward SLMs or hybrid routing where it helps
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  • Hybrid AI routing

    Policy-driven routing between SLMs and private LLMs so the right model answers each request - by cost, latency, safety, or quality thresholds you define. We keep policies explainable for compliance reviews and observable in production.

    Key deliverables

    • Routing policies and fallback rules
    • Latency and cost dashboards per route
    • Safe rollout and shadow-mode testing
    • Documentation for security and architecture reviews
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  • Agent orchestration

    Orchestrate workflows, RAG, and tools on top of the models you already run - so production automation does not depend on a brittle chain of public APIs. We design for reliability and clear ownership between model, retrieval, and integration layers.

    Key deliverables

    • Workflow and tool-integration design
    • RAG and retrieval patterns where appropriate
    • Production error handling and retries
    • Roadmaps with your platform owners for deeper automation
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Frequently asked questions

Placeholder answers for layout and SEO - refine with legal and sales before campaigns.

Where should we start if we are new to private SLMs?
Most teams begin with Build and Run: a scoped use case, clear acceptance metrics, and a first model in a non-production or limited-traffic environment. If you already know you need broader coverage than a compact SLM, the Private LLM path may be the better entry - see the journey diagram above.
When is Private LLM the right path instead of a smaller SLM?
When tasks need wider generalization before you can safely specialize, or when you must match behavior to a larger open or licensed model inside your boundary. We still plan for compression, routing, or SLMs downstream so costs and latency stay under control as traffic grows.
How long does a typical first delivery take?
Timelines depend on data readiness, infra access, and governance steps. Indicative ranges are summarized on the About page under Engagement model; every quote is scoped after discovery - nothing on this site is a fixed commitment.
How do we request a proof of concept or discovery call?
Use Request a Proof of Concept or Book a 30-min Discovery Call from the contact page (or site header). We will confirm scope, stakeholders, and security expectations before proposing a concrete plan.

Not sure which service fits? Start on the homepage or About for engagement context, then Request a Proof of Concept.