AI features that ship to production.
LLM features, autonomous agents, RAG, and data plumbing — wired into real products with real evals, real cost discipline, and real users. Not slideware.
Summarize Q3 churn drivers
Top 3 drivers: onboarding drop-off (38%), pricing friction (24%), feature gap vs Acme (18%).
Evals
passingCost / req
$0.004
4–12 wk
To first feature
100%
Eval-backed releases
2–10×
Cost reduction via routing
$50K+
Minimum engagement
Capabilities
From prompt to production.
We design AI features the way we design products: backed by evals, scoped by cost and latency, and shipped behind feature flags.
LLM features
Chat, copilots, summarization, classification, structured extraction. Built into your product, not bolted on.
- Streaming UIs
- Function calling
- Structured outputs
- Multi-model routing
Autonomous agents
Multi-step agents with planning, tool use, memory, and human-in-the-loop checkpoints where it matters.
- LangGraph & custom orchestration
- Tool use & function calling
- Long-horizon planning
- HITL checkpoints
RAG & retrieval
Production-grade retrieval over your private data: chunking, embeddings, hybrid search, reranking.
- pgvector, Pinecone, Weaviate
- Hybrid (BM25 + vector)
- Cross-encoder reranking
- Knowledge graphs
Data & ML pipelines
ETL, embeddings infra, batch and streaming inference, model fine-tuning when it earns its keep.
- Modal, Ray, Airflow
- Embedding pipelines
- Fine-tuning (LoRA)
- Eval data labeling
Methodology
How we actually ship AI.
Evals first
We build the evaluation harness before we build the feature. Quality regressions get caught in CI, not by your users.
Cost discipline
Per-request economics modeled from day one. Routing, caching, prompt compression, and model tiering when needed.
Safety & guardrails
PII redaction, prompt injection defense, output validation, jailbreak monitoring, and audit trails.
Latency budgets
P95 latency targets enforced. Streaming, parallelization, and speculative decoding where it earns the wait.
Walk-away
What you walk away with
- AI features in production with real users
- Evaluation harness that catches regressions in CI
- Predictable per-request cost and latency
- Safety guardrails: PII, prompt injection, output validation
- A team that knows what's actually shippable in 2026
The Jubile difference
What we don't ship
Demo-grade prompts that look great on Twitter
Production prompts evaluated on hundreds of cases
Single-model lock-in
Model routing across providers, swap in days
Surprise OpenAI bills
Per-request economics modeled and budgeted
"It worked when I tested it"
Eval suite, regression catches, monitored in prod
AI stack
Provider-agnostic by design.
We pick the right model for the job and architect to swap them when the frontier moves — which it will.
Models
Orchestration
Retrieval
Evals
Infra
Observability
Investment
From $50K
Fixed-scope or T&M. Most AI engagements land between $80K–$300K depending on scope.
Timeline
4–12 weeks
Thin end-to-end slice fast, then harden with evals, guardrails, and cost controls.
Hand-off
Eval suite included
You leave with a labeled eval set, runbook, observability, and the prompts under version control.
FAQ
Common questions
Can you actually build something useful, or is this another AI demo?+
We've shipped LLM-powered products in healthcare, mental-health analytics, and B2B SaaS that handle real users every day. We measure by features that survive production — not by Twitter screenshots.
Will it be locked into one model provider?+
No. We design for model routing from day one. You can swap GPT-5, Claude, Gemini, or open-weights based on cost, latency, and quality per use case. We've migrated production systems between providers in days, not quarters.
How do you keep costs under control?+
We model per-request cost in discovery and budget against it in production. Levers we use: model tiering, semantic caching, prompt compression, structured outputs, and evals to detect when a cheaper model is good enough.
How do you handle hallucinations and safety?+
Evals on a labeled set, structured outputs with schema validation, retrieval grounding for factual claims, PII redaction, prompt-injection defenses, output classifiers for high-risk surfaces, and audit logs end-to-end.
RAG, fine-tuning, or agents — how do you decide?+
Defaults: RAG when your data changes often or is too large for context; fine-tuning when style or schema matters and the data is stable; agents when the task needs multi-step planning with tools. We pick based on cost, latency, and quality — not hype.
Can you integrate with our existing product and data?+
Yes. Most engagements wire AI into existing web or mobile apps, with retrieval over your Postgres, S3, Notion, Drive, or warehouse. We handle access control, tenant isolation, and per-customer data boundaries.
What about EU AI Act, GDPR, or healthcare compliance?+
We ship to GDPR/CCPA-compliant environments by default and have shipped to HIPAA-aligned healthcare deployments. We'll structure data residency, model selection, and DPIA documentation around your jurisdiction in discovery.
AI in production
LLM features wired into real products with real users.
Evaluation pipelines, RAG, agents and cost/latency tuning — built for shipping, not demos.