Services

Three ways in, one way of working

Each line stands on its own. Most engagements start with strategy or a discovery, then move into implementation once there is a specific system to build — always embedded, never handed off as a deck.

01

AI Strategy

Where AI is worth applying, where it is not, and what order to tackle it in. Readiness, opportunity mapping, sequencing — a decision document, not a vision deck.

02

Implementation

Forward-deployed engineering: we build and ship the system embedded in your team, from discovery through pilot to a working embed. The flagship line.

03

Data & Analytics

Getting the data in shape before any model ships — pipelines, quality, and the unglamorous foundation everything else depends on.

What kind of consulting is DataTranquil?

DataTranquil designs and builds data platforms, analytics and AI systems that run in production, rather than delivering recommendations from the outside. Work spans three lines — AI strategy, implementation, and data & analytics — grounded in more than 25 years of hands-on enterprise delivery. Most engagements are scoped and priced as a piece of work with an end date; where a client needs the build to happen inside their own codebase, we deliver it forward-deployed, embedded with their team.

Capability matrix

What the practice actually does

Three groups of disciplines, not a list of buzzwords. Every engagement draws on some combination of these, embedded through forward-deployed delivery.

Capability matrix: architecture, engineering, and forward-deployed delivery disciplines
GroupWhat it covers
Architecture
  • Technical architecting — system design and integration topology across the client's existing stack.
  • Solution architecting — mapping a business workflow to a system shape that can actually be built and shipped.
Engineering
  • Data engineering — pipelines and transformation that keep a model fed with data it can trust.
  • Prompt engineering — prompt design and iteration tested against real inputs, not one good example.
  • Agent / harness engineering — the scaffolding around a model: tool definitions, state, retries, and routing.
  • Eval engineering — eval suites and golden sets that catch regressions before they reach production.
  • Context engineering — what gets retrieved, ranked, and assembled into a model's context window, and what gets left out.
  • Guardrail / lint enforcement surfaces — automated checks that fail a build on a violation, such as a claims-lint that blocks a false marketing claim before it ships.
  • Multi-agent orchestration — routing and coordination across multiple specialized agents working the same task.
Forward-Deployed Delivery
  • Embed with the team — engineers working inside the client's codebase, tools, and review process.
  • Pilot — a working version built against real data and real constraints, time-boxed with pass/fail criteria.
  • Full product build — taking the system from pilot to a production system the client's team can own.

Start here

Not sure which line fits?

An AI-readiness discovery is the fastest way to find out — a fixed-scope engagement that tells you where AI is worth applying before you commit to building anything.

Talk to an expertContact us