What is forward deployed engineering?

Engineers who work inside your team rather than behind a support queue. At Weld that means data engineers, with Weld's agents doing the legwork, who build, migrate or run your data platform end to end and make it ready for AI, in your warehouse, your repository and your Slack or Microsoft Teams.

What does an AI-ready data platform mean?

Data your agents can trust. Sources synced reliably into your warehouse, models tested and documented, every metric defined once, and agents given governed access, so any assistant, or an agent in Slack, answers from the same numbers as your dashboards.

How does the proof of concept work?

You pick one use case that matters, like revenue or spend efficiency, and the question you want an agent to answer about it. We build the whole chain for it, from ingestion to a dashboard and an agent you can ask, on free platform access. If it is the right fit, we scope the build and send you a quote. If it is not, there is nothing to unwind.

How long does it take?

A new platform is live in 2 to 6 weeks: sources and warehouse in the first week, models and metrics in weeks 2 to 3, dashboards, reverse ETL and agent access in weeks 3 to 5, and hand over by week 6. Migrations and audits of an existing stack are scoped on the first call.

It depends on your stack and what you need. Reach out and we come back with a quote that covers the platform, AI access, hosting and the work itself, scoped against what you need. Platform access during the proof of concept is free.

Can you migrate us from another tool or our own scripts?

Yes. We map what you run today, rebuild it in Weld, run both side by side until the numbers reconcile and then cut over. We keep your schemas and table names wherever your current tool allows, so your models keep running. Moving off Fivetran, the schemas match exactly and the migration is done at no cost.

We already have a warehouse, models and dashboards. Is this for us?

Yes. We keep your tools and work in them: audit what is there, fix the numbers people argue about, add one set of metric definitions and agent access, then run it. Weld usually takes ingestion, orchestration and reverse ETL, where it saves the most work.

Can an agent change our code or data on its own?

An agent can rerun a sync or a model, the same operational actions your own team can take in Weld. Changes to your code and models go through pull requests that an engineer reviews and someone on your team approves.

Which AI assistants does it work with?

Any assistant that supports the Model Context Protocol, from chat assistants to coding agents, and agents in Slack or Teams can answer from the same governed metrics. Each assistant acts with the permissions of the person using it.

Who owns the code and the data?

You do. Your data stays in your warehouse, and models and metric definitions are standard SQL and YAML in your own repository. If you ever leave, you take all of it with you.

How do you get access, and how do you keep it safe?

Through accounts you create and control, with the least access the work needs. Weld is SOC 2 Type II audited and GDPR compliant, changes go through pull requests, and you can revoke access at any time.

Get your AI agents into production

Forward deployed engineers and our own AI agents make your data ready for agents, then keep it running. Live in weeks.

Talk to an engineer

SOC 2 Type II
You approve every code change
Your code stays yours

Weld · your forward deployed team

# data-requestsSlack

L
Lena · CFOPerson
How is gross margin by channel trending this quarter? Paid social looks off.
Analytics agent AI agent
Gross margin is 43.6% this quarter, down 1.2 points. Paid social drove most of the drop.
Gross margin by channel, Q3governed metric
Channel Revenue Margin vs Q2
Paid search €412k 44.1% +0.4
Paid social €286k 33.7% −3.9
Email €198k 52.3% +0.8
Organic €351k 46.0% −0.2
Q2 Q3
What drove it
- Paid social: higher costs per click from August, margin down 3.9 points.
- Email: highest margin, and a growing share of revenue.
Break down paid social by campaignCompare with last yearSend this every Monday

Channel	Revenue	Margin	vs Q2
Paid search	€412k	44.1%	+0.4
Paid social	€286k	33.7%	−3.9
Email	€198k	52.3%	+0.8
Organic	€351k	46.0%	−0.2

AI agentDo the legwork, through Weld's MCP server

Intake agent
Turns a message into a scoped task
Slack, Microsoft Teams
Pipeline builder
Connects sources, sets up syncs
Shopify, HubSpot, Postgres, Stripe
Model builder
Writes models, tests and docs
dbt, SQLMesh
Reviewer
Checks tests, rows and numbers
Pull requests
Monitoring agent
Watches runs, fixes failures
Snowflake, BigQuery, Databricks
Analytics agent
Answers from governed metrics
Claude, ChatGPT, Slack

PersonDecide, review and approve. Nothing ships without them.

WWeld engineer
Reviews, decides, owns it
YYour team
Asks, and approves every merge

PersonWeld engineer09:20 Reviewed the answer and the metric behind it → definition confirmed with finance

Trusted by data and AI teams

AI-ready data

What agents need before they can answer “what is our revenue?” We build every layer.

Agents give different answers to the same question when they lack the context an experienced analyst carries in their head. An AI-ready platform writes that context down. Pick a layer to see what we do there.

The foundation Weld runs

Governance · agents act as the user

Layer

Metric definitions

Every metric defined once, so a dashboard, a spreadsheet and an agent calculate it the same way.

What we do

We define the metrics people ask about most, starting with one domain, usually finance.

Weld engineers
Weld platform

Works with your choice of

Cube
dbt Semantic Layer
LookML
Power BI models
Tableau
Omni
Steep
and more

The agent context stack, in depthRead the guide on the blog

Where you start

Three ways in. An AI-ready platform at the end of each.

Start from nothing, move off another tool, or keep what you have. Engineers and agents do the work in every case, and the same engineers stay on it afterwards.

Reference data stack300+ connectors150k+ syncs a day99.9% sync uptime

Applications

Connected

Shopify

HubSpot

Stripe

Ad platforms

Connected

Google Ads

Meta Ads

LinkedIn Ads

Databases

Connected

Postgres

MySQL

MongoDB

Weldengineers + agents

Ingest300+ connectors, real-time CDCNew

ModelTested models, run after every syncNew

MetricsEvery metric defined onceNew

ServeAgent access, data back to your toolsNew

Your warehouseNew

BigQuery

Snowflake

Databricks

Dashboards

New

Looker

Power BI

Metabase

AI agents

New

Claude

ChatGPT

Slack

Operational tools

New

Salesforce

HubSpot

Klaviyo

Everything is new, and every part of it runs on day one.New or addedYours, keptReplaced by Weld

An AI-ready data platform, from nothing, in 2 to 6 weeks.

For you if: No warehouse yet, or reporting spread across spreadsheets and the dashboards inside each SaaS tool.

AI outcome

Your agents answer from governed metrics, not from raw tables, from the first week the platform is live.

Week 1
Scope and connect
Sources agreed, warehouse stood up, first syncs running.
Weeks 2 to 3
Model and define
KPI framework, warehouse layers, every metric defined once.
Weeks 3 to 5
Report, activate, open to AI
Dashboards live, data flowing back to your tools, agents answering from governed metrics.
Weeks 4 to 6
Hand over and run
Training and documentation, then we keep operating it.

Platform and build in one quoteGet a quote

Move to Weld without breaking what already works.

For you if: On another ELT tool or home-grown scripts, and paying for it in money or in maintenance.

AI outcome

A migration is the cheapest moment to make data AI-ready: models documented, metrics defined once, agents connected to the result.

Step 1
Map
Every connector, model and dashboard you run today, and what depends on what.
Step 2
Rebuild
Connectors rebuilt in Weld, keeping your schemas and table names wherever your current tool allows, so your models keep running.
Step 3
Run in parallel
Both stacks side by side until row counts and metrics reconcile.
Step 4
Cut over, then add AI
Switch when the numbers match, then add the metric definitions and agent access AI needs.

Migration help is included, and moving off Fivetran costs nothingHow the migration works

Keep your stack. Make it AI-ready, and keep it that way.

For you if: You already have a warehouse, models and dashboards, and not enough hands to keep them healthy.

AI outcome

Agents stop guessing from raw tables and answer from the same metrics your dashboards use.

Step 1
Audit
Sources, models, metrics and dashboards mapped, with what is broken and what agents cannot use yet.
Step 2
Fix what matters first
The numbers people argue about, as pull requests with the evidence attached.
Step 3
Make it AI-ready
Metrics defined once in a semantic layer, models documented, agents given governed access.
Step 4
Run
Agents watch every run. A named engineer owns requests, new sources and changes.

A named data engineer on top of the platformGet a quote

Proof of concept

You pick the use case. We prove it end to end, before you commit.

01
Choose the use case
One question that matters to the business, like revenue, spend efficiency or retention, and the one you want your agents to answer.
02
We build the chain
Ingestion, modelling in your warehouse, activation, a dashboard you can open and an agent you can ask.
03
Platform access is free
No licence cost while we prove the fit. Nothing to unwind if it is not right.
04
Then we scope the build
A quote first, with the scope agreed against it. Never an open-ended estimate.

Start with a proof of conceptFree platform access while we build it.

Customers

Small data teams, running like big ones. In their words.

“Weld is just the first building block in our AI vision. We need to ingest the data, we need a single source of truth, we need metrics defined, and in order to do that, we need a data platform.”

One source of truth
Metrics defined once
Finance reconciliation
AI agents in Slack

Laurenz BrockmeyerDirector of Data & Analytics at OMHU

Read the story

“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”

Michael HowesHead of Data & Insights at Dishoom

“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”

Sven HasenbergCFO, VitaMoment

How we work

Letting someone into your stack is a big ask. These are the rules we keep.

Agents propose, people merge
Agents work in pull requests and never push to your main branch. An engineer reviews every change, and only someone on your side merges it.
- Build passed
- Rows match source
- Revenue matches billing
Evidence with every change
The build result, the values we checked against and the numbers before and after, attached to the pull request.
No agent speaks for us
Agents draft. An engineer decides what gets said to your team, and answers for it.
your-org/analytics
- models/orders.sql
- models/margin.sql
- metrics/revenue.yml
- + metrics/churn.yml
Your code, in your repository
Models and metric definitions are standard SQL and YAML in your own repository, and pipelines run in your own Weld account.
In your channels
A shared channel in Slack or Microsoft Teams with the engineers doing the work. No ticket portal in between.
The platform team is down the hall
When a source changes its API, the people fixing it maintain the connector. No support ticket to a vendor.

Pricing

Scoped to your stack. Reach out for a quote.

Platform, AI access, hosting and the work itself, in one quote. Here is what it replaces.

Building it yourselfWith Weld

People
Building it yourself: Hire a data engineer, months before the first pipeline
With Weld: Engineers and agents from week one
Ingestion
Building it yourself: An ELT tool, often billed per row, rising with your data
With Weld: 300+ connectors, flat pricing, maintained by us
Models
Building it yourself: Transformation licences, plus an orchestrator to run them
With Weld: Models built, tested and run after every sync
Metrics and BI
Building it yourself: A semantic layer and a BI tool to license, host and patch
With Weld: One set of metric definitions and dashboards, hosted and run by us
AI access
Building it yourself: Agent access and permissions to build and secure yourself
With Weld: Agents with governed access, acting as each user
Operations
Building it yourself: Someone on call when a sync fails at 3am
With Weld: Agents watch every run, an engineer owns the fix
Billing
Building it yourself: A salary and a stack of vendor invoices
With Weld: One quote, scoped to your stack

After the build

Keep a named data engineer on the project.

New pipelines and models, new reporting domains and new agent use cases, whenever you need the capacity, without making a full-time hire.

Get a quoteTell us about your stack and we come back with a scope and a quote.

FAQ

Questions we get asked. Before the first call.

Still have a question?

Ask one of our engineers

Six agents do the legwork through Weld's MCP server: an intake agent scopes requests from Slack or Teams, a pipeline builder sets up syncs, a model builder writes models and tests, a reviewer checks the numbers, a monitoring agent watches every run and an analytics agent answers questions from your metrics. Engineers decide what to build, review every change and own the result. You talk to the engineers.

Pick one agent.
We'll get it into production.

Free platform access while we build the proof of concept, and a quote before anything else.

What do you need? Pick any.

Talk to an engineer See the platform

Get your AI agents into production

What agents need before they can answer “what is our revenue?” We build every layer.

Agents and assistants

Instructions

Governed access

Metric definitions

Business context

Modelled data

Reliable data

Three ways in. An AI-ready platform at the end of each.

An AI-ready data platform, from nothing, in 2 to 6 weeks.

Move to Weld without breaking what already works.

Keep your stack. Make it AI-ready, and keep it that way.

You pick the use case. We prove it end to end, before you commit.

Choose the use case

We build the chain

Platform access is free

Then we scope the build

Small data teams, running like big ones. In their words.

Letting someone into your stack is a big ask. These are the rules we keep.

Agents propose, people merge

Evidence with every change

No agent speaks for us

Your code, in your repository

In your channels

The platform team is down the hall

Scoped to your stack. Reach out for a quote.

Keep a named data engineer on the project.

Questions we get asked. Before the first call.

Pick one agent.
We'll get it into production.

Get your AI agents into production

What agents need before they can answer “what is our revenue?” We build every layer.

Metric definitions

Three ways in. An AI-ready platform at the end of each.

An AI-ready data platform, from nothing, in 2 to 6 weeks.

You pick the use case. We prove it end to end, before you commit.

Choose the use case

We build the chain

Platform access is free

Then we scope the build

Small data teams, running like big ones. In their words.

Letting someone into your stack is a big ask. These are the rules we keep.

Agents propose, people merge

Evidence with every change

No agent speaks for us

Your code, in your repository

In your channels

The platform team is down the hall

Scoped to your stack. Reach out for a quote.

Keep a named data engineer on the project.

Questions we get asked. Before the first call.

Pick one agent.We'll get it into production.

Pick one agent.
We'll get it into production.