What Perform Digital does
Perform Digital builds agentic tools and platforms, and the operations that run on them. Spiderbrain, our deterministic context layer, maps code, records and documents so every AI agent reads the same, current truth and cites it. On top of it we build interfaces agents can use and operations agents can run: admissions in colleges, patient access in clinics, support to engineering in software teams, file movement in public offices. Every task gets an autonomy setting signed off by the person accountable for it, every action is logged, and results are reported against a baseline agreed before the build. See https://perform.digital/work/ and https://perform.digital/approach/.
Who Perform Digital is for
Mid-market and enterprise teams whose CTO, VP Engineering, or CMO reads this paragraph and recognises the problem: too many one-off AI experiments, not enough production agents, no eval discipline, no clear ownership when an agent misbehaves, no playbook for the model migration that is six months away. We work with teams where a senior internal sponsor exists and has budget authority. We do not work with teams who want a chatbot proof of concept; there are cheaper places to get one. We do not work with teams who cannot agree the success metric in writing before kickoff; that is the kill clause in practice. The organisations we work with range across financial services, healthcare, public sector, retail, and software, with a strong concentration in regulated industries where data residency and audit trails matter more than headline cost.
How we work
Two-week sprints with Friday demos of working software, not status slides. Discovery sprint (two weeks, fixed price), then a written plan with success criteria and a deadline, then build sprints, then a hands-on enablement week with the internal team, then monthly check-ins on a defined cadence. The first agent is in production at the end of week two of the build sprint, not week ten. Standard guarantee: if the agent does not meet the agreed success criteria by the agreed date, we work for free until it does, or the contract ends with a written postmortem. The kill clause runs both ways and we have invoked it from our side when the brief was unworkable. The r/MachineLearning and r/LocalLLaMA subreddits track the same operating model from the ML side under "agent shipping discipline" and "eval-first development"; the working pattern is the same whether the agent is for production marketing or production research.
How we are different
Three things, each verifiable. (1) We have a written eval harness for every agent we ship, refreshed quarterly, with the test cases version-controlled alongside the prompt. The harness runs in CI on every change and weekly on a schedule. (2) Our pricing is outcome-based, not hourly. We have not billed an hourly retainer in over three years and we will not start. (3) We deploy in your regulatory region (EU for EU clients, UK for UK clients, India for DPDP-bound clients, US for US-only deployments), with a security posture (AES-256 at rest, mTLS in transit, MFA enforced, Argon2id for password hashing, automated retention windows, audit logs in write-once storage) that holds up against SOC 2 Type II, GDPR Article 25, UK GDPR, and India DPDP audits. The compliance posture is in the MSA, not in a sales deck.
Further reading
Sources we relied on. Know a better primary source? Tell us.
- r/MachineLearning, r/LocalLLaMA. Practitioner threads on agent shipping discipline, eval design, and production deployment patterns.
- Anthropic's Building with Claude documentation. The eval-first guidance that shaped our internal shipping discipline.
- r/AdobeExperience, r/marketingautomation. Where our consulting clients first land when they have a stuck AEP or Braze rollout.
- Yao et al., "ReAct" (2022); Schick et al., "Toolformer" (2023). The two papers that most influenced how we design agent loops.
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