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Production-grade AI systems,
engineered to be trusted.

We help teams design, evaluate, and deploy reliable AI — from RAG pipelines to agentic workflows — with the eval rigor to ship and the observability to scale.

Request a consultation → See what we do
// what we do

Four practices, one outcome: AI that ships and stays shipped.

We embed alongside your team to turn experimental notebooks into systems you can hand to customers and put on a roadmap.

AI Evaluation Frameworks

Robust offline + online eval suites that measure accuracy, safety, and business impact — before regressions hit prod.

RAG System Design

Retrieval pipelines tuned for your data — chunking, hybrid search, re-ranking, and grounding strategies that hold up at scale.

Agentic AI Systems

Multi-step agents that plan, call tools, and reason through workflows — wrapped in guardrails and traceable from end to end.

Content Annotation

Human-in-the-loop pipelines for labeling, preference data, and alignment — designed for quality, throughput, and audit.

// engineering rigor

Evals that match the way users actually behave.

Most teams ship the demo. We help you ship the system — with the metrics, dashboards, and replay infrastructure to know it's still working at 3am.

  • Behavioral test suitesAdversarial, regression, and safety probes wired into CI.
  • Production replaySample real traffic, score it, route findings back into training data.
  • Human + LLM judgesCalibrated rubrics that combine model graders with expert review.
eval_suite.py 
# twotower.ai — production eval harness
from twotower import Eval, Judge, Trace

suite = Eval("support-agent-v3")

suite.add(
  name="groundedness",
  judge=Judge.llm(rubric="cite_or_decline"),
  threshold=0.92,
)

suite.add(
  name="tool_safety",
  judge=Judge.human(panel="sme"),
  threshold=1.00,
)

result = suite.run(traces=Trace.replay(
  source="prod", sample=2_000,
))

if result.regressed:
  result.block_release()
else:
  result.ship(channel=CANARY)
// expertise

Deep technical surface, end to end.

Foundation model ecosystems, production ML, and large-scale data pipelines — the boring parts that make AI products actually work.

Evaluation & Metrics

Quantify what's actually shipping — and what's slipping.

  • LLM benchmarking
  • Human preference modeling
  • Safety & hallucination detection
  • Online A/B + interleaving

Retrieval Systems

Get the right context to the model, every time.

  • Vector databases
  • Hybrid search (BM25 + embeddings)
  • Re-ranking strategies
  • Query understanding

Agent Architectures

Plan, act, recover — and stay in the lines.

  • Tool orchestration
  • Planning & reasoning loops
  • Observability & guardrails
  • Cost & latency tuning
// our approach

From diagnosis to durable production.

A pragmatic four-phase engagement designed to outlive the consulting contract.

01 / DIAGNOSE

Diagnose

Audit your current AI stack, data flows, and the gaps between demo-quality and production reliability.

02 / DESIGN

Design

Architect scalable systems matched to your use case — from retrieval to agents — with eval baked in.

03 / DEPLOY

Deploy

Ship production-ready pipelines with monitoring, evals, and on-call playbooks from day one.

04 / ITERATE

Iterate

Close the loop with feedback data, replay, and continuous improvement your team owns.

Build AI systems
you can actually trust.

Tell us what you're trying to ship. We'll tell you what it'll take to get there — and whether we're the right team to help.

Start a conversation → See our work