Services
Hands-on engineering for the problems your roadmap can't absorb.
Buildience is an engineering and consulting partner. This page covers the areas we work in, the ways you can engage us and how a piece of work runs.
Forward deployed engineers
An engineer who joins your team and owns a piece of the problem.
A forward deployed engineer (FDE) works inside your organisation rather than at the end of a requirements document. They take part in your planning, work in your repositories and talk to the people affected by the problem. Then they take responsibility for a defined area until it works.
How they work with your team
- 01
Learn the context
Time with the people doing the work, the systems involved and the history behind them.
- 02
Agree the scope
A written description of the area they own, what done looks like, and who makes decisions.
- 03
Build alongside you
Work happens in your tools, with progress visible in your own repositories.
- 04
Hand over
Documentation and pairing so your team can run and extend what was built.
Challenges this suits
- Features that keep slipping because the team is stretched
- Integrations that touch several systems and teams
- Internal tools nobody has had time to build
- Putting an AI prototype into a real workflow
- Stabilising a product after a period of fast growth
Good fit when
- The problem is clear, but the solution still needs working out
- The work depends on your existing systems and people
- You want the knowledge to stay with your team afterwards
Probably not when
- You need permanent headcount: an FDE supports your team, it doesn't replace hiring
- The work is fully specified and self-contained: a defined project is usually simpler
- Nobody on your side has time to answer questions or make decisions
How this differs from outsourcing a task
| Outsourcing a task | Forward deployed | |
|---|---|---|
| Starting point | Outsourcing a task:A specification written up front | Forward deployed:The problem, explored with your team |
| Where the work happens | Outsourcing a task:The vendor's process and tools | Forward deployed:Your repositories, planning and channels |
| When requirements change | Outsourcing a task:A change request | Forward deployed:A conversation, then an updated scope |
| At the end | Outsourcing a task:A handover of finished code | Forward deployed:Working software your team already knows |
Product and MVP development
From an idea worth testing to a product people can use.
An MVP should answer a specific question about your business. We help work out which question matters most, then build the smallest product that answers it.
01Discovery
Who the first users are, what they need, and the assumption that would hurt most if it were wrong.
02Technical planning
Architecture, data model and build-vs-buy decisions sized for the first version, with room to grow.
03Build
Frontend, backend and the integrations the product needs, delivered in short increments you can try.
04Testing
Automated tests where they protect what matters, and real users trying it early.
05Iteration
Changes based on what users do, and a plan for the next release.
What's in scope depends on your goals and what we agree together.
A good starting point if
- You're a founder or product team with a defined problem to test
- You need a first version without building a full team first
Frontend, backend and full-stack engineering
Interfaces, APIs and the logic in between.
Most features cut through every layer: a screen, an API, some business rules and a database. We work across all of them, in new applications and in codebases that already have a history.
Interface
Interfaces
Accessible, responsive frontends that match how people use the product.
API
APIs and services
Clear contracts, input validation, and error handling that doesn't leak internals.
Logic & data
Application logic and data flows
Business rules that are easy to find, test and change, with data moving predictably between components.
Complete web applications
From an empty repository to a deployed application, when that's what the work needs.
Maintainability
Readable code, sensible structure and tests, so the next feature is not harder than the last.
AI engineering
AI features that work with your data and your software.
Practical AI work is mostly integration work. A language model becomes useful when it can reach the right information, respect who is allowed to see what, and hand its output to the systems and people who need it.
Specialist area: scope confirmed case by case.
- 01
Your app and data
Where the information and the request come from.
- 02
Context and permissions
Only the data this task needs, for the people allowed to see it.
- 03
Model
A hosted language model, called through its API.
- 04
Check the output
Validation before anything reaches a person or system.
- 05
Person or workflow
The output lands where the work happens.
What this covers
AI features in existing products
Summaries, search, drafting or classification added to software your users already work in.
Model integrations
Connecting hosted language models to your application through their APIs, with limits on cost and failure.
AI in internal workflows
Assistants and tools that take on a defined, repetitive part of a process, with a person reviewing where it matters.
How we approach it
- 01
Start with a specific task
Find a specific, repeated piece of work where AI or automation would clearly help, and define what a good result looks like.
- 02
Connect to real systems
Integrate with your APIs, databases and SaaS tools, giving each workflow access to only what it needs.
- 03
Check quality before scaling
Test outputs against real examples before rolling out, and keep a person in the loop where mistakes are costly.
We integrate existing AI models. We don't train proprietary models or do AI research.
Automation and system integrations
Fewer manual steps between the tools you already pay for.
When information has to be copied from one system to another by hand, mistakes and delays follow. We connect systems so data moves reliably, and automate the repetitive steps around them.
API connections
Integrations with third-party platforms, including authentication, retries and rate limits.
Cross-system workflows
Processes that span your CRM, billing, support and internal tools, triggered by real events.
Process automation
Repetitive checks, updates and notifications handled by software, with clear logs when something fails.
Custom integrations
Purpose-built connections when an off-the-shelf connector doesn't cover what you need.
Signs it would help
- Someone on your team reconciles data between systems every week
- A process breaks whenever one person is away
Claude, skills, MCP and developer workflows
AI coding tools set up around how your engineers work.
AI coding assistants help most when they understand your codebase, conventions and internal tools. This is a specialist area where we can help an engineering team set that up.
| Capability | What it involves |
|---|---|
| Claude-based development workflows | What it involves: Ways of working with Claude in day-to-day engineering: reviews, refactoring, test writing and documentation. |
| Custom skills and reusable instructions | What it involves: Written instructions and skills that capture your team's conventions, so results are consistent. |
| MCP server and connector development | What it involves: Model Context Protocol servers that give AI tools controlled access to your internal systems and data. |
| Plugins and tool integrations | What it involves: Connecting AI tools to the issue trackers, repositories and services your team already uses. |
| Developer workflow automation | What it involves: Automating repetitive engineering chores around builds, reviews and releases. |
Existing product problem-solving
Bring us one problem. You don't need a whole new project.
A single issue is a reasonable place to start: a bug nobody can pin down, a page that got slow, or a feature the team never had time for. We start by understanding the code as it is, then fix things in steps that keep the product running.
| Area | Where we start | |
|---|---|---|
| 01 | Debugging | Where we start: Reproducing intermittent issues, tracing their cause, and fixing them with a test that keeps them fixed. |
| 02 | Performance | Where we start: Measuring where time goes, then fixing the parts that users notice. |
| 03 | Technical debt and modernisation | Where we start: Upgrading frameworks and untangling fragile code gradually, without a risky rewrite. |
| 04 | Feature improvements | Where we start: Finishing or extending features in an existing codebase, following its conventions. |
| 05 | Engineering bottlenecks | Where we start: Slow builds, painful releases or missing tests that make every change expensive. |
Ways to engage
Pick the shape that fits the work.
Every engagement is agreed in writing before it starts. Pricing, duration and terms depend on the scope, so we discuss them once we understand what you need.
Forward deployed engineers
Engineers working inside your team on an agreed area of responsibility.
Explore an engineering engagement →A defined project
A specific outcome with a written scope and a clear end point.
Discuss a technical challenge →MVP or product build
A new product taken from scoping to a first release, then iterated.
Build your MVP →Integration or automation
Connecting systems or automating a process, usually as a defined project.
Talk about an integration →Ongoing collaboration
Continued improvements after a first piece of work, if that's useful to you.
Discuss your project →
How a piece of work runs
- 01
Understand the problem
A conversation about what's happening, who it affects and why it matters now.
- 02
Define scope and constraints
What's in and out, the systems involved, and limits on time, budget or data.
- 03
Agree the approach
A written plan with the first increment, how progress is shown, and who decides what.
- 04
Implement and validate
Build in small steps, test against the agreed definition of done, and deploy.
- 05
Review and decide what's next
Look at the result together, then stop, continue, or plan the next piece of work.
Questions about services
Describe the problem and we'll suggest one. As a rough guide: if the solution still needs working out and depends on your systems and people, forward deployed engineers tend to fit. If you can describe the result precisely, a defined project is usually simpler.
No. AI is one area among several. Much of the work described on this page, such as integrations, debugging or building a web application, involves no AI at all, and we'll only suggest it where it clearly helps.
Both, but the advice is grounded in implementation. We'll recommend an approach and explain the trade-offs, and we can then build it with you.
A description of the problem and someone who knows it well. Once we agree to work together, we'll also need access to the relevant systems and a person who can make decisions on your side.
Have a technical challenge in mind?
Tell us what you're trying to do and what's in the way. We'll reply with questions and how we'd approach it.
What happens next
- 1
We read your message and reply, often with a few questions.
- 2
If it looks like a fit, we suggest a call to understand the context.
- 3
We follow up with how we'd approach it.