AI agent consulting & development
We design and ship AI agents that operate like employees, chief of staff, ops manager, marketer, SDR, researcher, support, running 24/7 inside your stack. Agentic consulting for teams replacing or augmenting headcount, and a build practice that turns the roadmap into shipped systems.
Agentic systems are not chatbots. A chatbot answers a question and stops. An agent is given a goal, decides what to do next, calls tools, reads and writes to your systems, hands off to a human when it should, and keeps going until the work is done. That difference is what makes them useful as employees rather than as widgets. A chief of staff agent that reads your inbox, drafts responses in your voice, updates the CRM, and puts a shortlist on your desk at 8am is a fundamentally different thing than a helper bubble on a website. We build the second kind.
We work in two modes. Agentic consulting is where teams start when they know they need this but do not yet know what to build. We audit the org, map the workflows that are eating the most senior time, model the ROI of augmenting versus replacing versus keeping a role human, choose the tools and the stack, and produce a roadmap the team can execute against with or without us. Build is where we design, ship, integrate, monitor, and iterate the agents themselves, from single-purpose agents that automate one workflow to multi-agent systems that coordinate across a function.
The agent menu is broad on purpose. Chief of staff and executive assistant agents that run the inbox, calendar, and prep cycle. Operations and workflow agents that watch systems, catch exceptions, and route work. Marketing agents that draft, edit, and publish against a brand and content plan, run campaign ops, and iterate ad creative. Sales development agents that enrich, sequence, and follow up without dropping the ball. Research and analyst agents that read, summarize, and cite. Customer support agents that handle tier one and hand off cleanly. Recruiting, finance, and back-office agents that take the repetitive parts of the job and leave the judgment calls for the humans.
Under the hood we stay vendor-neutral. We build on the LLM providers and orchestration frameworks that fit the job, wire in retrieval and memory so agents actually know your business, and integrate against Slack, email, CRM, calendars, Notion and Drive, internal APIs, and browser automation where the work lives. Every agent ships with an evaluation loop, human-in-the-loop checkpoints where the stakes require them, guardrails against the failure modes that matter, and cost controls so the bill does not surprise anyone. Outcomes are framed in terms teams actually care about, headcount leverage, cycle-time reduction, coverage expansion, quality lift, not benchmark scores that never make it out of the deck.
Further reading: agentic AI insights — including our practical guide for business leaders and the ROI framework.
What it solves.
- Hiring is slow, expensive, and still leaves senior time buried in repetitive work.
- The team wants leverage from AI but is stuck between demos, prompts, and hype with no roadmap.
- Coverage windows (nights, weekends, spikes) are broken because they depend on humans being awake.
- Existing AI tools are point solutions that do not talk to each other or to the actual systems the team runs on.
What ships.
- Agentic audit: workflow map, ROI model, and prioritized roadmap
- Agent design: role, tools, memory, guardrails, and eval plan
- Build and integration into Slack, email, CRM, calendars, Notion/Drive, and internal APIs
- Human-in-the-loop and approval flows for high-stakes steps
- Monitoring, evals, cost controls, and iteration cycle
- Team enablement so operators can extend and supervise the agents
Who it is for.
- — Founder-led teams trying to buy leverage instead of headcount
- — Lean ops and RevOps teams standardizing repetitive work
- — Agencies and studios who want senior output without linear hiring
- — PE-backed rollups standardizing back office across portfolio companies
- — Executives who want a chief of staff agent before they hire the human
The method.
Audit the work, then pick the agent
We start with the workflows, not the tools. What is eating senior time, what is on-call at midnight, what is the org afraid to hire for. That map decides which agents get built first and which stay on the roadmap.
Design for supervision, not autonomy for its own sake
Every agent has a role, a scope, tools it can call, and checkpoints where a human sees the work before it goes out. Autonomy is earned by the eval loop, not assumed on day one.
Ship into the stack you already run on
Agents live in Slack, email, the CRM, the docs, the calendar, the internal APIs. Not in a separate app the team forgets to open. Integration is where most agentic projects die and where we spend most of our time.
Instrument, evaluate, iterate
Every agent ships with evals, cost controls, and a monthly review that decides whether to expand its scope, tighten its guardrails, or retire it. Agents are software, not magic.
Marketing Grade
Marketing Grade is an owned NU HAUS product that puts our agentic approach in the open. A visitor pastes a business URL and the system runs an automated multi-dimension marketing audit against that site.
The output is a prioritized report the buyer can act on and a lead-capture workflow that hands qualified interest back to a human. It is the same pattern we design for corporate teams: an agent handles the repeatable analytical work, a human owns the judgment call and the follow-up.
Common questions.
- When does an AI agent make more sense than a new hire?
- When the work is repeatable, well-scoped, high-volume, and currently blocking a senior person, an agent is usually the right first move. Roles with heavy judgment, negotiation, or relationship weight stay human, often with an agent supporting them. We use the audit to draw that line explicitly, function by function, before anyone commits to a build.
- How ready does our team need to be to start an agent project?
- You need a decision-maker who can approve access to the systems the agent will touch, a written view of the workflow you want to change, and someone internal who can own supervision after launch. An in-house ML team is not required to begin.
- What does a pilot scope and timeline typically look like?
- A first agent is usually scoped to a single workflow inside a single function and takes four to eight weeks from kickoff to a supervised production run. That includes discovery, design, build, integration, evaluation setup, and a first review cycle. The intent is a real agent operating on real work, not a demo.
- How do agents integrate with our existing tools and data?
- Agents connect to the systems the work already lives in: email and calendar, Slack, CRM (Salesforce, HubSpot), docs (Notion, Google Drive), ticketing, internal APIs, and browser automation where an API does not exist. We prefer official APIs and per-agent credentials with least-privilege scopes so access can be audited and revoked cleanly.
- How do you handle security, data privacy, and governance?
- Every project starts with a data map: what the agent can read, what it can write, where prompts and outputs are logged, and who can review them. We prefer providers with enterprise data terms, avoid training on client data, keep secrets in a managed vault, and set retention windows against your policy. Governance decisions are documented so audit and legal can review them without a translation layer.
- Where do humans stay in the loop, and how is that enforced?
- Human-in-the-loop is a design decision, not a slider. High-stakes steps (external email, financial actions, customer-facing publishing) require approval before the agent proceeds. Lower-stakes steps run autonomously and surface in a review queue after the fact. Enforcement lives in the orchestration layer, not in a prompt, so the agent cannot skip a checkpoint even if it decides it should.
- How do we evaluate ROI before committing to a full build?
- We model ROI at the audit stage against three numbers you already have: the loaded cost of the hours the agent will absorb, the opportunity cost of the senior time it frees, and the coverage the agent adds where a human is currently a bottleneck. Those numbers get compared against build cost, runtime cost, and supervision cost. If the model does not clear a threshold you set, we say so and stop.
