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Kernelstack.ai Kernelstack.ai
System Status: Production Ready

We build LLM systems
you can trust.

Production-grade AI engineering. We turn fragile prototypes into reliable, scalable infrastructure with rigorous evals and cost controls.

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What we believe

Our engineering principles for building reliable LLM applications in a non-deterministic world.

rule
gavel

Evals are non-negotiable

We do not ship vibes. Every change is measured against a rigorous evaluation dataset to prevent regressions and hallucination drift.

speed
bolt

Latency + cost are features

Model capabilities mean nothing if the UX is sluggish or the unit economics are upside down. We optimize token usage and caching strategies.

shield
lock

Security in the harness

Prompt injection and data leakage are real threats. We build guardrails directly into the orchestration layer, not as an afterthought.

Capabilities

memory Evals
memory RAG
memory Guardrails
memory Observability
memory Cost Tuning
memory Hardening

How we work

From audit to production in weeks, not months.

1

Audit

2 Weeks

We analyze your current stack, identify bottlenecks, and map out the evaluation strategy.

2

Sprint

4-6 Weeks

Rapid engineering cycles to implement RAG, set up evals, and ship the v1 production system.

3

Retainer

Ongoing

Continuous monitoring, model fine-tuning, and adapting to new SOTA models as they release.

What you get

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CI/CD Gates

Automated evaluation pipelines that block regressions before merge.

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Operational Runbooks

Clear documentation for incident response and model degradation.

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Full Metric Dashboards

Real-time visibility into latency, cost, and user feedback.

CASE STUDY: FINTECH

40%

Cost Reduction

By optimizing prompt context windows and implementing semantic caching for a high-volume fintech client.

BEFORE: $0.04 / req AFTER: $0.024 / req

Technical FAQ

Do you host our models?add
No. We engineer the infrastructure inside your cloud environment or VPC. You retain full ownership of your data, weights, and IP.
What stack do you prefer?add
We are agnostic but opinionated. Common deployments include LangChain or LlamaIndex, vector stores such as Pinecone or Weaviate, and custom evaluation harnesses.
How do you handle data privacy?add
We implement PII masking middleware before data ever hits an LLM provider and can help deploy open-source models inside your environment when needed.

Ready to ship?

Stop prototyping in notebooks. Start building reliable systems that scale.

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Free 30-min consultation | No commitment