Managed LangChain · AI transformation consulting

AI that keeps working after the demo.

LangYield runs your LangChain and LangGraph applications as a managed service, and helps your company decide what to build with AI, in what order, and what it should return.

Talk to us What's included

support-agent · productionExample

LangGraph1.0.4 · upgraded 12 Octcurrent

LangSmith tracing100% sampled · 14d retentionon

Eval suite142 cases · 97.9% passgreen

Model routingSonnet, GPT fallback · 0.4%healthy

Cost, this month$2,310 · −18% vs Septemberunder budget

P95 latency3.8 s · target 3.0 swatching

On-call24×7 · 2 pages, 0 incidentscovered

The monthly yield report a managed client receives. Figures are illustrative.

We build and operate on

LangChain
LangGraph
LangSmith
OpenAI
Anthropic
AWS Bedrock
Azure OpenAI
Google Vertex
pgvector · Pinecone · Weaviate

What we do

Two services, one team.

Most companies have an AI prototype that impressed someone. Few have one that runs reliably, costs what was planned, and is measured against the number it was meant to move. We close that gap from both ends.

Managed LangChain

We run what you've built, or what we build with you.

Your LangChain and LangGraph applications, operated like production software: kept current, observed, evaluated, and on-call.

Version upgrades and dependency management across LangChain, LangGraph and model SDKs
LangSmith tracing, dashboards and alerting
Evaluation suites and prompt regression testing on every change
Cost, latency and model routing tuned monthly
Incident response with a named engineer and an SLA
A monthly yield report your leadership can read

AI transformation consulting

From "we should do something with AI" to a funded roadmap.

We work with your leadership and your teams to find where AI pays back, and to say plainly where it does not.

Use-case discovery and value sizing with the people who do the work today
Build, buy or wait decisions, with vendor and model selection
Reference architecture, data readiness and security review
Governance: what the model may do, who signs off, how it is audited
Team enablement so your engineers own the result
A sequenced roadmap with expected returns per initiative

Build to production

Agents, retrieval and workflows delivered as code you own.

When the roadmap says build, we build it in your repository, with your conventions, and hand it over or keep running it.

LangGraph agents with human approval steps where they belong
Retrieval over your documents and systems, with evaluated answer quality
Back-office automation that integrates with the tools your teams already use
Evals, tracing and cost controls from the first commit
Handover to your team, or straight into the managed service

How we work

Short steps, each with a number attached.

Every phase ends with something you can measure and a decision you can make. Nothing moves to the next step on enthusiasm alone.

Assess

2 weeks

We map your workflows, data and current AI experiments, and come back with the three to five places where AI would return the most, each with a sized estimate.

Pilot

6 to 8 weeks

One use case, built on LangChain and LangGraph in your environment, with an evaluation suite that says whether it works before anyone has to take our word for it.

Production

Per use case

Hardening, tracing, cost controls, security review and rollout to real users. Your engineers are in the room throughout, so the code is theirs on day one.

Managed operations

Monthly, ongoing

We keep it current, watch it, tune it and answer the pager. You get a monthly report with the number the project set out to move.

Managed service

What "managed" means here.

A fixed monthly scope, so there is no debate about what is covered when something breaks at 2 am.

Area	Included every month	Why it matters
Upgrades	LangChain, LangGraph, LangSmith SDK and model API changes applied, tested and rolled out	The framework moves fast; a release six months old is already a risk.
Observability	LangSmith tracing on every run, dashboards, alert thresholds you agree with us	You see what the model did, not just that it answered.
Evaluation	A maintained eval set, run on every change and on a schedule against live traffic samples	Quality drift is caught in a report, not in a customer complaint.
Cost and routing	Monthly review of token spend, caching, model choice and fallbacks	Most stacks we take over cost 20 to 40% more than they need to.
Incident response	A named engineer, a response SLA, a written post-mortem for anything user-facing	Someone who already knows your system answers the page.
Security and access	Key rotation, prompt-injection review, data-handling checks on each release	Models get new capabilities; your controls have to keep up.
Reporting	One monthly yield report: usage, quality, cost, incidents and the business number it moves	Leadership gets an answer to "is this working?" without a meeting.

Why LangYield

Yield is the metric.

We are measured on what the AI returns to your business after it ships, not on how good it looked in the first meeting.

One team from roadmap to on-call: The people who help you choose what to build are the people who run it afterwards. Advice gets honest fast when you have to operate it.
You own everything: Code in your repository, data in your accounts, model contracts in your name. Leaving us should be a handover, not a migration.
LangChain specialists, not generalists: We work in LangChain and LangGraph every day, track every release, and know which abstractions hold up in production and which to avoid.
We say no to the wrong use case: Some problems are better solved with a rule, a form, or a person. Telling you so early is part of the service.

Tell us what you are trying to do.

A short note is enough. We reply within one business day with a yes, a no, or the questions we would need answered first.

hello@langyield.com

Prefer a call? Say so in the message and we will send a few times.