Services / Implementation
Forward-deployed engineering
We don't hand off a specification and wish you luck. We embed inside your team and build the system with you, until it's running in production and your people can own it.
What is forward-deployed engineering?
Forward-deployed engineering means the consulting team embeds directly inside the client organization to build and ship the system itself, rather than handing off a specification for someone else to build. DataTranquil engineers work alongside your team through discovery, pilot, and embed, so the AI system ships in your production environment.
How it runs
Discovery, pilot, embed
Three stages, in order. Each one produces something real before the next begins.
- 01
Discovery
We work with your team to define the specific system worth building — the workflow it touches, the data it depends on, and what "working" means in your environment.
- 02
Pilot
We build a working version against real data and real constraints, not a demo. It gets used, tested, and corrected before anything is scaled.
- 03
Embed
The system moves into production alongside your team, with our engineers working inside your codebase and process until your people can run and extend it without us.
Why embedded
Slideware doesn’t survive contact with a real codebase. The reason implementation is the flagship line here is that most AI work fails in the gap between a recommendation and a working system — the part where someone has to actually write the integration, handle the messy data, and get it past your security review.
Embedding closes that gap. Our engineers sit inside your team, in your tools, against your real systems, so the thing that ships is the thing that was scoped — not a prototype that quietly dies after the engagement ends.
Capability hats
What gets built, concretely
This is the line where the engineering hats actually run. Not a list of technologies — the disciplines a working system depends on.
| Hat | Applied in this line |
|---|---|
| Agent / harness engineering | The scaffolding around a model — tool definitions, state, retries, and routing — that turns a prompt into a system. |
| Data engineering | Pipelines and transformation that keep the system fed with data it can trust once it is live. |
| Prompt engineering | Prompt design and iteration tested against your real inputs, not a demo example. |
| Eval engineering | Eval suites and golden sets that catch regressions before they reach production. |
| Context engineering | What gets retrieved, ranked, and assembled into the model’s context window at runtime. |
| Guardrail / lint enforcement surfaces | Automated checks that fail a build on a violation — the mechanism, not a policy document. |
| Multi-agent orchestration | Routing and coordination across multiple specialized agents when one model isn’t the right shape for the task. |
FAQ
Common questions
How long does an implementation engagement run?
It depends on the scope agreed in discovery. A pilot is usually the fastest way to a working system; embed continues only as long as it takes to hand the system off to your team in a state they can own and extend on their own.
Do we need a strategy engagement first?
No. If you already know what you want built, implementation can start directly with discovery. A prior AI strategy engagement helps when the target system is not yet clear, but it is not a prerequisite.
Get started
Ready to build the actual system?
Most implementation engagements start with an AI-readiness discovery to confirm scope before discovery begins.