30% less time per routine process.
Measurable. Fixed price. Live in 60 days.

Yes. We work with three methods that require no vendor cooperation: browser automation, Computer Use for desktop applications, and internal API reverse-engineering. The last is particularly efficient — it works with the internal APIs every web application already uses, at a fraction of the cost of screen-based automation. Research across 94 enterprise domains showed a 100% success rate with this approach. In our experience, 'no public API' is a vendor policy, not a technical barrier — and vendor policies don't block our builds.

Because every AI session starts from zero. The model has no memory of your firm unless you provide structured context in that session. A set of markdown files — defining your company, your clients, your offer, your output preferences, and what you never do — changes this immediately. When we build agents for clients, this context architecture is the first thing we create. It is the difference between AI that writes like a generic assistant and AI that writes like a senior member of your team.

Match model to task complexity. Flagship models are expensive and necessary for complex reasoning. For classification, routing, status checks, and standard drafts — which make up the majority of most AI workflows — lightweight models produce equivalent output at 10-20% of flagship cost. In the agent stacks we build, every stage runs on the appropriate model for its function. We also implement session structure to prevent context accumulation costs. In most implementations we review, these two changes reduce total AI spend by 60% or more.

Not by waiting for stability. The firms that will lead in two years used this period to build AI-native processes — not in response to a crisis, but ahead of one. In practice, this means mapping where your team spends time on work AI can do at lower cost and building workflows to redirect that capacity. The goal is not headcount reduction — it is senior talent doing senior work, with AI handling the surrounding layer. We run this mapping process in our initial audit.

Because the firms that moved in 2024 and 2025 already have operating AI processes — they are not waiting. Entry-level analyst and research roles in consulting and finance are already being replaced, not because firms want to cut headcount, but because the economics no longer support the old model. The technology is mature enough for production use. The risk now is not moving too early — it is moving too late to build a process advantage before competitors close the gap.

A triggered workflow runs when someone asks it to. An autonomous agent runs on its own — monitoring conditions, detecting changes, and acting without being prompted. In practice: a triggered workflow generates a report when you request one. An autonomous agent monitors your pipeline, flags anomalies, and delivers a briefing whether or not anyone remembered to check. We design agents to be autonomous and continuous where the use case supports it. The monitoring layer — the work that requires someone to pay constant attention — is where autonomous agents create the most immediate and measurable value.

Yes, and we have built exactly this. A three-agent pipeline handles lead discovery, website research, and personalised email outreach — running overnight, storing all results in a shared database, and reporting back in the morning. One agent finds companies from public directories. A second extracts context from each company's website. A third writes and sends personalised outreach based on your defined offer. The setup takes one session. After that, the pipeline runs independently.

Entry-level research, structured analysis, document review, and first-draft preparation are the most directly affected — not in the future, but now. These tasks concentrate in analyst and junior associate profiles and are where AI performs at or above junior professional quality in our implementations. Senior judgment, client relationships, and complex synthesis remain human-dependent. The firms thinking clearly about this are not cutting teams — they are rebuilding delivery models around what their senior talent does best, with AI handling the surrounding work.

Give the AI structured context about your business before it starts any task. A set of markdown files — covering who you are, how you write, who your clients are, what your offer looks like, and what you never do — fed to the AI at session start produces output that reflects your firm, not a generic template. We build this context architecture in the first engagement. Once it exists, every AI agent in your stack has full business context from the first prompt. Without it, every session starts from zero and the outputs show it.

Invest in the planning phase before execution starts. In our builds, the AI produces a detailed plan before generating any output. We review and correct that plan until it matches the requirement exactly — this alone eliminates most rework. We also use structured context files that specify output format, constraints, and explicit negative instructions for each task. Negative constraints — what the AI must not do — are particularly effective at preventing the most common errors. Specific always beats vague.