Platform
Seven interlocking systems. Adopt one to start, or stitch the full stack together.
A managed inference stack that takes the rough edges off serving frontier and open-weight models — KV-cache reuse, speculative decoding, batched routing, traffic splits, and SLOs that actually hold under burst.
Talk about this systemCompute-layer infrastructure for teams running real training and inference workloads — bin-packing across heterogeneous clusters, preemption-aware scheduling, spot integration, and cost telemetry that closes the loop.
Talk about this systemWe build retrieval systems where chunking, embedding choice, hybrid search, and re-ranking are tuned to the eval that matters: did the user get the right answer. Plus the pipelines to keep them fresh.
Talk about this systemWe treat the prompt layer like infrastructure. Vertotech's Prompt-as-a-Service generates and versions industry-tailored prompts — finance, healthcare, legal, retail, manufacturing — graded against your golden set and distributed to every product surface that needs them.
Talk about this systemAgentic systems blow past the boundaries of traditional app servers. We build the sandboxes, the durable workflow engines, and the guardrails that let agentic products go to production without the cost or safety blow-ups.
Talk about this systemTrace-level observability that captures prompts, retrievals, tool calls, and outputs — paired with eval pipelines that run in CI and in production, on synthetic and real traffic alike.
Talk about this systemWe work alongside your security, legal, and product teams to build governance that fits the way you actually ship — model access controls, prompt-injection hardening, data-flow review, audit logging, and the policy artifacts compliance reviewers actually accept.
Talk about this systemProcess
We map the workload — latency budget, throughput, cost ceiling, eval — to the right slice of the platform.
Senior engineers stand the system up alongside your team. No offshoring, no junior-only delivery.
Latency, cost-per-call, eval scores, and SLO compliance — instrumented from day one.
Hand-off, retainer, or fully-managed — your call. We document everything either way.
FAQ
Both. We deploy into your AWS, GCP, or Azure account when you need data-locality or your own commit. We also run a managed control plane for teams that want the platform without owning the ops.
Open-weight (Llama, Mistral, Qwen, DeepSeek), closed-API (Anthropic, OpenAI, Google), and the long tail in between. The routing and observability layers are model-agnostic.
Most inference or retrieval stand-ups run 4–8 weeks to a production-grade endpoint. Larger programs (multi-region inference, agentic platforms) scope from 8 weeks up.
Yes — most engagements are collaborative. We embed with your engineers, ship in your repos, and pair on the parts we don't own.
Fixed-fee for scoped stand-ups. Retainer or usage-based for managed services. We come back with a proposal within two business days of an initial call.
A running, instrumented system in your accounts (or ours) with playbooks, dashboards, and an on-call story. Plus an architecture writeup your team can defend in design review.