Cost, timeline, ownership and lock-in — and the vocabulary, so the rest of the site makes sense.
There is no fixed price list. Cost depends on scope, and you get a written proposal with the number in it before you commit to anything.
The sequence is deliberate: a free 30-minute discovery call, then a working prototype and a written proposal covering timeline, cost, and the metric the build is meant to move. You see a real number attached to a real thing rather than an estimate attached to a description.
What moves the number is scope — how many systems have to be integrated, whether the process is well documented, and whether the first build is one workflow or several.
The first working build is typically live in two to four weeks. Larger programmes extend from there in weekly sprints.
Work is scoped so that something reaches production early rather than a single long build ending in a reveal. The first useful thing runs while the rest is still being built.
Thirty minutes, free, no pitch and no obligation. We map your processes and pinpoint where AI and automation create the most value.
No preparation needed, and you do not need a spec, a budget, or a technical person on the call. Bring the process that annoys you most.
By the end you will know whether there is anything worth building — including when the answer is no.
Yes. Every build is handed over versioned, monitored and documented, and it runs on infrastructure you control.
Lock-in is usually accidental rather than contractual: undocumented systems, credentials only one party holds, architecture nobody else understands. Handover is treated as part of the build, so moving the work in-house or to another team stays a decision you can make.
Then you have spent weeks rather than quarters finding out, and you are under no obligation to continue.
That is the point of leading with a prototype instead of a roadmap. An unconvincing prototype is a cheap, useful answer.
An AI agent is a system that takes actions in your tools to complete a task, rather than only generating text about it.
The distinction that matters in practice is tool access. A model that can only produce words can describe how to update an order. An agent with controlled access to your order system can actually update it, then report what it did.
The engineering work is mostly in the boundaries: what the agent may do on its own, what needs confirmation, and what must go to a person immediately.
Automation follows a path you defined in advance. An agent decides the path at run time. Automation is better when the process is fixed; an agent is better when the input varies.
| Automation | AI agent | |
|---|---|---|
| Decides | You, in advance | The system, at run time |
| Best for | Fixed, well-defined processes | Varied input, judgment in the middle |
| Behaviour | Identical every run | Varies with the input |
| Cost per run | Effectively nothing | Model cost per invocation |
| Failure mode | Stops, visibly | Can proceed confidently and be wrong |
| Testing | Deterministic | Needs evaluation, not just unit tests |
Most real systems are both. A workflow runs deterministically until it hits the step that genuinely needs judgment, and that step calls a model. Reaching for an agent where a rule would do is a common and expensive mistake — it costs more per run, it is harder to test, and it can fail in ways a rule cannot.
RAG — retrieval-augmented generation — is a pattern where the system searches your own documents first and answers using only what it found, citing the source.
The practical consequence is that answers are checkable. Each response points back to the passage it came from, so someone can verify it rather than trust it. For contracts, policies and compliance material, that is the whole value.
More on this under RAG over documents.
An MCP server is a standard way to give an AI model controlled access to a specific tool or data source, so the same integration works across different models and clients.
Before a shared protocol, every model-to-tool connection was bespoke. MCP makes the connection reusable, which matters when you do not want your integration work tied to whichever model you picked this year.
Buy when a product already fits your process. Integrate when the pieces exist but do not talk. Build only when the process is genuinely specific to how you work.
| Buy | Integrate | Build | |
|---|---|---|---|
| Fits when | A product matches your process | The tools exist but are disconnected | The process is specific to you |
| Time to value | Fastest | Weeks | Weeks, then ongoing |
| Ongoing cost | Licence per seat | Low, once built | You own maintenance |
| Main risk | Bending your process to the tool | Brittleness at the seams | Building what you could have bought |
We will tell you when buying is the right answer, including when that means a smaller engagement for us. A signal worth trusting: if the current workaround is a spreadsheet several people maintain by hand, the process is real and specific, and building usually pays for itself.
This is most of what an AI strategy and prototyping engagement is for.
No. Most engagements start from a described annoyance rather than a specification.
A napkin sketch, a “what if we could…”, or a bottleneck you are sure software could solve is enough to start. You do not need a spec or a technical co-founder to find out whether it is viable.
Usually yes, and integration assumes you keep the systems you have rather than replacing them.
CRM, ERP, email, operations tooling and data warehouses are the normal case. Systems without an API are also common and still workable — through scheduled exports, file drops or direct database access. Older line-of-business software is often exactly where the manual work accumulates.
More under CRM and system integration.
We operate remotely from the Greater Houston area and work with clients throughout the United States. Being nearby changes only whether we can meet in person.
For businesses in Houston and Sugar Land an in-person first meeting can usually be arranged — see the Houston page. Being outside the area costs you nothing in service.
Yes. Small and local businesses are one of four groups we work with, alongside mid-market and enterprise, startups and product teams, and agencies and professional services.
There is no minimum size and no required industry. The requirement is a process you can describe.
Access to the people who actually do the work, and someone who can make decisions.
The most common cause of a slow project is not technical. It is waiting on access, or on a decision nobody is empowered to make. Time with the people running the process is worth more than any document.
Email hello@devautomationlabs.ai or use the contact form. We reply within one business day.
The first step is a free 30-minute call. See how we work for what happens after it.
Email hello@devautomationlabs.ai and we will answer it — we reply within one business day.