AI developer experience that ships more, safely.
Rolling out coding copilots, internal AI tooling and the guardrails around them, so your engineers get real leverage from AI without leaking code or IP. We evaluate honestly, integrate into your SDLC, and measure impact with DORA metrics and adoption, not hype. Own the platform, swap the model.
Built for teams that want AI leverage without the risk.
AI developer experience earns its place when engineers are already reaching for AI tools and leadership needs it to be governed, measured and safe. These are the situations where a deliberate rollout pays for itself, whether you are a fast-growing product company or an established business modernising how it builds software.
Copilots spreading unmanaged
Engineers have already turned on assistants individually, with no policy on data, plans or which repositories they touch. You need a governed rollout before shadow usage becomes shadow risk.
Knowledge trapped in repos and docs
Answers live across code, wikis and runbooks nobody can find fast. You want code-aware chat over your own sources with permission-aware retrieval, not another public chatbot.
IP and secret leakage worries
Security and legal are nervous about code, secrets or customer data flowing into third-party models. You need clear data boundaries and an audit trail before wider adoption.
Pressure to show AI ROI
Leadership has invested in AI tooling and wants honest evidence it is working. You need DORA and adoption measurement against a baseline, not a vendor slide.
Slow, inconsistent delivery
Review queues, flaky tests and toil are eating cycle time. You want AI woven into the SDLC and golden paths so the fast way is also the safe way.
Building internal AI tools
You are standing up PR review assistants, incident copilots or internal MCP tools and want them built on a platform you own, with the model as a swappable component.
Evaluate honestly, integrate deliberately, measure everything.
The failure mode with AI tooling is buying licences, declaring victory and never checking whether anything improved. We do the opposite: pick a pilot cohort, wire tools into the real workflow behind proper guardrails, and measure against a baseline before spending goes wide. Where a tool does not earn its keep, we say so and drop it.
Principles we hold to on every rollout.
- Own the platform, swap the model. The integration, retrieval and guardrails are yours; the underlying model is a component you can change as trade-offs shift.
- Data boundaries come first. No training on your code, permission-aware retrieval, secret scanning and repository allow-lists are designed in before anyone gets access.
- Retrieval respects existing permissions, so an engineer only ever sees context they are already entitled to see.
- Impact is measured against a baseline with DORA metrics and genuine adoption, never a headline productivity percentage.
- We are honest about where AI does not help, and we will recommend against tooling that adds noise rather than leverage.
What an AI developer experience engagement covers.
Copilot rollout & evaluation
Tool and plan selection, a pilot cohort, editor and CI integration, and a measured evaluation so adoption is a decision, not a default.
Code-aware internal chat
Retrieval-augmented chat over your repositories, docs and runbooks with permission-aware access, so answers cite your own sources.
PR review & incident copilots
Assistants that summarise diffs, flag risky changes and help during incidents, wired into the tools your team already lives in.
Internal MCP tools
Model Context Protocol tools that expose your internal systems to assistants safely, with scoped permissions and audit built in.
Guardrails & governance
Data boundaries, secret leakage prevention, model choice, self-hosted versus API trade-offs, and logging for a full audit trail.
Measurement & golden paths
DORA and adoption dashboards against a baseline, plus AI woven into golden paths so the paved road is the safe one.
AI developer experience sits alongside our broader AI development and agents work, and leans on the delivery discipline behind shipping faster. The golden paths it plugs into are built with platform engineering, and the same rigour carries through to production AI development.
Our AI developer experience process.
Four phases, planned backwards from measurable, governed adoption. The architecture call confirms exact scope and guardrails before any tool reaches an engineer.
Baseline & boundaries
Capture current DORA metrics and workflow pain, define data boundaries and model choice, and pick the pilot cohort.
Pilot & guardrails
Stand up copilots, code-aware chat and guardrails for the cohort, with permission-aware retrieval and audit logging in place.
Measure & decide
Compare delivery and adoption against the baseline, keep what works, drop what does not, and report honestly on both.
Scale & own
Roll proven tooling into golden paths and the wider team, with everything as code so you own the platform and swap the model.
The tools we actually use.
No reseller agreements and no partner quota. Recommendations follow your workflow, data sensitivity and budget. Typical building blocks on an AI developer experience engagement:
Coding assistants
Editor copilots and agentic coding tools evaluated on your codebase, chosen on plans that never train on your code.
Retrieval & RAG
Vector search and permission-aware retrieval over repositories, docs and runbooks, so context stays scoped to entitlements.
Models & hosting
Frontier hosted APIs, private endpoints or self-hosted open-weight models, kept model-agnostic so you can swap as needed.
MCP & integration
Model Context Protocol tools and APIs that connect assistants to internal systems with scoped, auditable permissions.
Guardrails & audit
Secret scanning, prompt and output filtering, repository allow-lists and full logging for a defensible audit trail.
Metrics & SDLC
DORA and adoption instrumentation wired into CI and the platform, so impact shows up next to delivery, not in a spreadsheet.
AI developer experience FAQ.
What is AI developer experience?
AI developer experience is the practice of putting AI-assisted tooling into the daily workflow of an engineering team so that people ship more with less friction, safely. In practice it covers coding copilots in the editor, code-aware chat over your own repositories and documentation, PR review and incident assistants, and the guardrails, golden paths and measurement that make all of it trustworthy. Done well it is a platform decision, not a licence purchase: the goal is a repeatable, governed way for engineers to use AI on real work, not a scattering of tools nobody trusts.
Do coding copilots actually make teams faster?
Sometimes, and honestly not always. Copilots help most with boilerplate, tests, unfamiliar APIs and small well-scoped changes, and they help least on deep architectural work or in large, unusual codebases where suggestions are more likely to be wrong. The gains are real but uneven, and raw acceptance rate is a poor proxy for value. We roll tools out to a pilot cohort, measure delivery and review quality against a baseline, and keep only what moves the numbers. We will tell you plainly where AI tooling is not paying off.
How do we stop AI tools leaking our code or secrets?
By designing data boundaries before rolling anything out. That means choosing tools and plans with no training on your code, keeping retrieval permission-aware so a developer only ever sees context they are already entitled to, scanning prompts and outputs for secrets, and enforcing allow-lists on which repositories and endpoints tooling can reach. Where the sensitivity warrants it we run self-hosted or private-endpoint models so nothing leaves your boundary, and every AI interaction is logged so usage can be audited.
Should we self-host models or use an API?
It depends on your data sensitivity, latency needs, budget and how much platform capacity you have to run inference. Hosted APIs give you the strongest frontier models with almost no operational burden and are the right default for most teams, provided the contract forbids training on your data. Self-hosting or private endpoints make sense when regulation, IP concerns or cost at scale demand that inference stays inside your boundary. We keep the architecture model-agnostic so you own the platform and can swap the model as the trade-off shifts.
How do you measure the impact of AI tooling?
Against a baseline, using DORA delivery metrics and genuine adoption rather than vanity numbers. We look at deployment frequency, lead time for changes, change-failure rate and time to restore before and after rollout, alongside how many engineers actually keep using a tool week over week and where it helps in the workflow. We deliberately avoid headline productivity percentages, because they are easy to game and rarely survive contact with reality. The aim is evidence you can defend, not a marketing figure.
Ready to get real leverage from AI?
Start with the readiness scorecard, or book a free 30-minute architecture call. A senior engineer reviews your workflow and constraints and returns a governed, measurable plan for rolling out AI developer tooling.