LLMOps & GenAI

What we ship

Six capabilities, one cohesive LLM platform.

Click through to see what each layer looks like in practice.

Retrieval-augmented generation that actually works in production

Most RAG pipelines look great in a demo and crumble at 10k QPS. We build the full stack: ingestion, chunking, embedding, hybrid search, reranking, and context assembly — instrumented end-to-end.

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Hybrid searchBM25 + dense + reranker (Cohere, Voyage). +20–35pp recall vs vanilla.
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Smart chunkingLayout-aware, semantic, late-chunking. We pick by data type, not by trend.
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Vector DBsPinecone, Weaviate, Qdrant, pgvector, Turbopuffer. Right-sized for your recall/cost target.
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Continuous reindexingCDC-driven freshness, embedding versioning, A/B between embedding models.

SourceDocs · DB · Confluence · S3

ParseUnstructured · LlamaParse

ChunkSemantic / layout-aware

EmbedOpenAI · Voyage · BGE

IndexPinecone · Qdrant · pgvector

Hybrid retrieve + rerankBM25 + dense + Cohere Rerank

Generate & citeLlama 3.1 / GPT / Claude with citations

Fine-tuning when prompts hit a ceiling

We help you decide: prompt? RAG? LoRA? full fine-tune? distillation? Each has a different ROI curve. Once chosen, we run the data prep, training, evaluation, and rollout.

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LoRA / QLoRA on Llama, Mistral, Qwen, GemmaFor domain adaptation and tone control. 10–100× cheaper than full fine-tune.
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Distillation pipelinesTeacher (frontier model) → student (open weights) with eval-gated rollout.
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RLHF / DPO / KTOPreference tuning when a single SFT pass isn't enough.
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Synthetic data & RLAIFBootstrap training data when human labels are scarce or expensive.

# LoRA fine-tune on Llama 3.1 70B with Axolotl
base_model: meta-llama/Llama-3.1-70B-Instruct
load_in_4bit: true
adapter: qlora

datasets:
  - path: "s3://acme-mlops/support-tickets/v3"
    type: chat_template

lora_r: 32
lora_alpha: 64
learning_rate: 0.0002
num_epochs: 3

eval_steps: 100
eval_table_size: 200

# Promote to registry on eval pass
on_eval_pass:
  register: "mlflow://acme/support-bot"
  tag: "candidate"

Evals are the new tests

If you can't measure quality, you can't ship safely. We design eval harnesses that combine deterministic checks, LLM-as-judge, retrieval metrics, and human-in-the-loop samples.

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Gold sets & LLM-as-judgeCurated expected outputs, scored by judge models (we calibrate judge agreement vs human ≥ 92%).
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Retrieval recall & faithfulnessDid we retrieve the right context? Did the answer stay grounded? Both, scored.
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Adversarial & safety evalsGarak, PyRIT, custom jailbreak corpora. Run on every promotion.
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Production traffic evalSample live traffic into a shadow eval pipeline for regression detection.

eval-run · candidate vs prod

Suite	Score	Δ
gold-set (n=1,400)	94.2%	+0.3pp
retrieval recall@5	87.4%	+1.9pp
faithfulness	96.8%	+0.7pp
jailbreak resistance	99.1%	+1.4pp
latency p99	612ms	−44ms
cost / 1k req	$0.18	−$0.31

🟢 PROMOTION CLEARED — auto-deploying canary @ 5%

Guardrails keep AI on-script

Input filters, output filters, schema enforcement, jailbreak detection, PII redaction. Layered, fast, and observable — because every blocked response is a logged event.

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Input & output filtersNeMo Guardrails, LlamaGuard, Guardrails AI, Lakera. Composed in pipelines.
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Schema-validated outputsPydantic / JSON-schema enforcement, retry-on-fail with structured output.
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PII detection & redactionPresidio, custom regex + ML detectors. Pre-LLM and post-LLM passes.
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Jailbreak & injection detectionReal-time classifier. Logged as security events — alert your SecOps team automatically.

User input

Input guardrailPII redact · injection detect · topic filter

RAG + LLMRetrieval · context · generation

Output guardrailSchema · toxicity · faithfulness · PII leak

PassReturn to user

FailRefuse / retry / escalate · log event

Serving stacks that don't bankrupt you

vLLM, SGLang, TensorRT-LLM, TGI on the open side. Managed Bedrock, OpenAI, Anthropic, Vertex on the closed side. Routed intelligently with caching and quota management.

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vLLM & SGLang on KubernetesContinuous batching, paged attention, prefix caching. 2–5× throughput vs naive serving.
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Semantic + KV cache30–55% hit rates on production traffic. Pays back in weeks.
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Smart routingLiteLLM gateway: route by cost, latency, quality, or quota. Failover across providers.
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Quantization & speculative decodingFP8, INT4, draft+verify. Quality protected by eval gates.

# Cost router: cheap → expensive on quality drop
routes:
  - name: support-bot
    strategy: cascade
    tiers:
      - model: "haiku-3.5"
        accept_if: "confidence > 0.85"
      - model: "llama-3.1-70b-tuned"
        accept_if: "confidence > 0.92"
      - model: "sonnet-4.6"
        fallback: true
    cache:
      semantic: true
      ttl: 3600
    quotas:
      tier_1_max_qpm: 8000

# Result: 64% cost cut, +0.3pp eval score

Prompts are code. Treat them like it.

Versioned, reviewed, tested, and shipped through the same pipeline as your models. Anything else and you're leaving production quality up to whoever last edited the file.

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Prompt registryLangSmith, Humanloop, PromptLayer, or custom on Git. With variables, versioning, A/B.
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PR-gated prompt changesEval diff visible in code review. No "I just tweaked the prompt" surprises.
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Multi-environment promotionStaging → canary → prod, same flow as model artifacts.
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ObservabilityEvery prompt + variable + response captured (with PII filtering) for replay and audit.

PR · prompt change · "improve refund flow"

- You are a polite support agent.

+ You are a support agent. Be concise.

+ For refund requests, follow the 3-step

+ verification protocol in {{policy_doc}}.

Eval

+2.1pp

Tokens

−18%

Latency

−110ms

The production stack
for large language models.

Six capabilities, one cohesive LLM platform.

Retrieval-augmented generation that actually works in production

Fine-tuning when prompts hit a ceiling

Evals are the new tests

Guardrails keep AI on-script

Serving stacks that don't bankrupt you

Prompts are code. Treat them like it.

Our default LLMOps stack — opinionated, but never married.

"We have a working POC.
How do we get to production?"

Six capabilities, one cohesive LLM platform.

Retrieval-augmented generation that actually works in production

Fine-tuning when prompts hit a ceiling

Evals are the new tests

Guardrails keep AI on-script

Serving stacks that don't bankrupt you

Prompts are code. Treat them like it.

Our default LLMOps stack — opinionated, but never married.

"We have a working POC.How do we get to production?"

"We have a working POC.
How do we get to production?"