From AI Prototype to Production: What Twig Forward Deployed Engineers Actually Ship

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Most enterprise AI teams don't have a model problem.

They have a production engineering problem.

The prototype works. Then real users arrive.

Retrieval misses the right document.
Agents take the wrong tool path.
Latency jumps under load.
Prompt changes create regressions.
Nobody can explain why an answer failed.

Twig Forward Deployed Engineers work directly with your team to turn these systems into measurable, production-ready infrastructure.

What we work on

RAG and Retrieval

We help teams improve:

• chunking and indexing architecture

• hybrid search: dense + sparse retrieval

• query rewriting and expansion

• metadata filtering

• reranking

• document freshness and invalidation

• multi-source retrieval

• citation and grounding quality

• retrieval caching

The outcome isn't "better RAG."

It's measurable improvement in:

Recall@K, answer correctness, retrieval precision, latency and cost per query.

Agent Architecture

Production agents need more than a system prompt and a collection of tools.

We help design:

• deterministic control flows

• tool routing

• state management

• retries and fallback paths

• human escalation

• structured outputs

• guardrails

• memory boundaries

• multi-agent orchestration

• failure recovery

The goal is to move from:

"The agent usually does the right thing."

to:

"We can predict, inspect and test what the agent will do."

Evals and Observability

This is often the first thing we build.

We create:

• golden datasets

• retrieval evals

• answer-quality evals

• tool-use evals

• multi-turn conversation evals

• regression tests in CI

• production tracing

• model and prompt comparisons

• drift detection

Now every release has numbers behind it.

Instead of:

You can say:

Latency and Cost

AI architectures accumulate latency quickly:

query rewrite → embedding → retrieval → reranking → LLM → tools → LLM

We profile the entire path and optimize:

• parallel execution

• streaming

• model routing

• semantic caching

• precomputation

• context compression

• retrieval depth

• tool-call architecture

The outcome is a defined latency and cost budget your team can actually operate against.

What an engagement looks like

Week 1 — Baseline

We instrument the current system and establish metrics.

Example:

• Retrieval Recall@10: 68%

• Answer correctness: 74%

• Tool success rate: 82%

• p95 latency: 7.4s

• Cost/query: $0.11

Now we know where the system is actually failing.

Weeks 2–4 — Fix the bottlenecks

Depending on the problem, we work directly in your codebase to improve retrieval, agent orchestration, evals, latency or infrastructure.

Every change is measured against the baseline.

Week 4+ — Production hardening

We add the things prototypes usually don't have:

• regression testing

• retries

• fallbacks

• monitoring

• drift detection

• load testing

• runbooks

The deliverable isn't a recommendation deck.

It's production code, eval infrastructure and measurable improvement.

How to get started

Start with a 30-minute technical architecture review.

Bring:

• your current architecture

• one or two production problems

• your existing evals, if you have them

• latency or quality metrics

• examples of failures you can't explain

We'll help identify whether the bottleneck is retrieval, orchestration, models, data, infrastructure or evaluation.

If the problem is well-defined, we move into a short diagnostic or embedded engineering sprint.

If you don't need us, we'll tell you that too.

→ Book a technical architecture review

Chandan Maruthi — Founder & CEO, Twig