What Perform Digital does
We ship production AI agents in under two weeks. Ready-made agents for copywriting, marketing campaign drafting, design briefs, and HR comms (from $50 per week, hosted, eval harness included). Custom Second Brain agents over your knowledge base using RAG with re-ranking, hosted in your regulatory region (from $1,200 per month, with versioned prompts and a written eval set). Multi-channel Sales Agent that runs across email, LinkedIn, and inbound chat with shared memory (from $300 per month). Predictive Legal Agent for contract review, counsel-reviewed and counsel-supervised for enterprise (custom pricing). Server-side observability agents for production AI workloads (custom). Full bespoke agent builds against a written specification for clients with a defined use case and an internal champion. We also consult and train on Adobe Experience Platform, Adobe Journey Optimizer, Customer Journey Analytics, Braze Canvas Flow, and the Salesforce Data Cloud and Agentforce stacks.
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 35+ Fortune 500 clients we have shipped to 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
Real, named sources the editor can swap in for specific URLs. We do not auto-link these because the right link changes over time. If you find a great primary source, write us and we will update the note.
- 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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