People‑first AI solutions
for your team's daily work and bigger bets:
more capabilitycapability,
less surveillanceanxiety.

Your engineering team is probably already using AI — some noticeably more productive, some shipping subtle bugs they didn't catch, most somewhere in between with no shared norms and a quiet unease underneath about what it means for their craft. Two weeks close that gap by rebuilding the SDLC around AI assistance, with the engineers' judgement kept at the centre.

Week 1 is observation and design. We sit with the team and watch how they actually use AI coding assistants across the lifecycle. We map every phase — requirements, design, implementation, review, test, deploy, observe — find where AI accelerates real work and where it slips in silent regressions, and co-design the team's AI-augmented SDLC: eval gates in CI, regression suites tuned to your codebase, prompt and context versioning, telemetry on agent-touched changes, and review and rollback practices for AI-assisted PRs.

Week 2 is practice and handover. Real PRs go through the new setup, we calibrate failure modes with the senior engineers, hand over the runbook, and retro on what holds and what needs more tuning. The seniors decide what counts as good and shipped; we instrument it. We also keep an eye on the human side — making sure no one feels quietly replaced or measured against the wrong yardstick — because a practice the team resents is a practice the team routes around.

Your team leaves with an AI-augmented SDLC that holds under load and that the team actually follows, working evals catching regressions in CI before they merge, telemetry that shows which assistance is helping and which is hurting, and the confidence that AI-assisted work in your codebase stays reviewable, reversible, and recognisably theirs. To help it bed in, we include bi-weekly check-ins through the following quarter.

Plenty of leadership teams stay stuck talking about AI for quarters. This sprint moves you past that in two weeks. The aim is deliberately sober: a real working model for your leadership, a clear-eyed look at one of your own workflows, and a first step you can stand behind — no theatre, no system half-built and abandoned.

Week 1 is the working model and discovery: how modern AI actually works underneath, where it fails, agents versus pipelines, what keeping a human in the loop buys you, and how to read vendor claims. We map your cross-functional workflows and, just as importantly, name who each change touches — whose work shifts, whose role feels exposed, and what your people are already worried about. We then pick one workflow with real leverage to examine closely.

Week 2 is applied and human: we prototype the chosen workflow with AI in the loop, run it against real data, and review together — including how it would land with the people who'd actually use it, and where People-First principles draw lines you won't cross. The week closes with a decision day: a realistic roadmap your team has aligned on, with a clear, scoped first step rather than an over-promised pilot.

Your leadership leaves with shared technical fluency, a worked example on one of your real workflows, a roadmap the team can execute, and the human concerns out in the open with a plan for each. We include bi-weekly check-ins through the following quarter to keep the momentum honest.

Most AI roadmaps come from somebody who read about AI, not somebody who's spent a decade shipping software and shaping product strategy. Two weeks flip that: we audit your roadmap, your team's workflows, and your operational drag — then identify which AI moves fit your business first.

Week 1 is discovery: we interview product, engineering, ops, sales, and leadership; read your roadmap and quarterly plans; map where your team's time goes and where the friction sits. Week 2 is roadmap synthesis: we bring senior product-and-engineering judgment to your situation — each candidate AI move weighed against your stage, team capacity, risk tolerance, technical debt, and cultural fit — and converge on a prioritized list with the trade-offs spelled out, not hidden.

You walk away with a written report covering five to twelve specific AI moves (yes/no/later for each), an honest read on which would land in your culture, and a recommended sequence for the next two quarters.

Skip the interview theatre. Replace CV roulette, take-home tests, and rounds of behavioural interviews with one exercise that actually tracks how someone will perform: 90 minutes working through a realistic imitation of the role — shaped around what the job genuinely demands — alongside someone from your team, observed against a framework your managers run themselves.

No AI scoring. No algorithmic ranking. No automated rejection. Part of the work is unteaching the reflex to automate the judgement. We show your managers why CV scorers and ranking models quietly bake in yesterday's bias, optimise for proxies that have little to do with the work, and turn people away on signal that was never predictive — and why a person watching a candidate handle a faithful imitation of the role is both fairer and a better forecast.

Two weeks install this for one role family. Week 1 is model design: we sit with your hiring team, agree what good actually looks like in the work itself, distil it into a 90-minute exercise that imitates the role, and build the observation framework that keeps the assessment consistent. Week 2 is pilot: trial sessions with real candidates, calibration against genuine signal, and manager training in facilitating rather than interrogating.

You walk away with a working hiring model you run for every role in that family, a manager runbook, the observation notes — and a team that understands why this judgement stays with people instead of an algorithm.

Most competitive intelligence in an SME is one person screenshotting LinkedIn posts into Slack. We replace that with a structured system your team owns and operates — close enough to your strategy and sales work that it earns a hands-on engagement rather than a drop-in tool.

We start with system design: working with your strategy and sales leads to pin down the competitors that matter, the signals worth watching (product launches, hiring patterns, pricing moves, partnerships, shifts in marketing language), and the cadence you'll keep. Then we build and hand over — a lightweight stack of feeds, watchers, and a structured workspace, training for your team on the operating discipline, and the first cycle run together so the rhythm sticks before we step away.

You walk away with a working competitive intelligence system your team runs on their own, the first briefs already delivered, a watchlist of the competitors and signal types that actually matter to your strategy, and the weekly cadence already turning.

Most AI policies are written by legal, signed by the CEO, ignored by everyone else. One month produces a policy your team co-authored — meaning they trust it, follow it, and surface the cases the policy needs to cover before they become incidents. EU AI Act alignment included.

Week 1 is surfacing: facilitated workshops with cross-functional groups to surface where AI anxiety is real and what use cases people are exploring quietly. Week 2 is use-case mapping: catalog the real use cases, classify against risk and value. Weeks 3-4 are drafting and co-authoring: we draft in plain language with your team in the room, work through disagreements, align with EU AI Act obligations.

You walk away with a signed AI policy your team actually wrote, a working library of approved and disallowed use cases, an escalation path for novel cases, and a workforce that's calibrated rather than scared.

If you want the full overhaul — your hiring practice rebuilt as an organizational capability, across role families — this is the engagement. One month installs a working hiring practice built around right-fit signal across your top two role families. No AI scoring. No algorithmic ranking. No automated rejection.

Week 1 — hiring process audit. We walk through every step, identify where signal is weak, where candidate experience is corrosive, where AI scaffolding would actually help.

Week 2 — policy co-development. A working hiring policy codifying People-First principles in language specific to your business, signed off by the people who will operate it.

Weeks 3-4 — build and train. Two trial environments matching your top two role families, pilot sessions with real candidates, hiring-manager training, the runbook that becomes your operating standard.

You walk away with a hiring practice that respects candidates as people, judged on work shaped like the role, two working trial environments your team owns, and a hiring policy that's signed and lived.

Building organizational AI capacity sits between a one-day workshop and a year-long degree. Six weeks produce shared AI judgment across a team of ten to twenty-five: weekly 90-minute live sessions, async readings between, hands-on exercises that compound.

Modules build on each other: how modern AIs actually work, failure modes, how to harness the stochastic nature of models in deterministic ways, augmentation versus automation (the People-First filter applied), safety and security basics, ethics in practice (what human-in-the-loop means when it costs you something), operating with agents day-to-day, building organizational judgment. Each session pairs concept with hands-on work using your actual context — no generic exercises.

Your team leaves with shared technical fluency that holds up in real decisions, working AI practice in their day-to-day, and the organizational judgment to evaluate the next wave of AI vendors without calling us back. Built for 10-25 participants per cohort.