Norton is a hyper-specialized AI boutique. AI staff augmentation, AI delivery pods and AI consulting. Eight roles across the AI lifecycle, one vetting gate, US-based teams.
Who we are
Most firms added "AI" to a menu of forty skills. Norton removed the other thirty-nine. Every recruiter, every lead and every engineer here has one job: getting AI systems into production for clients who cannot afford a science project.
We do staff augmentation when you require help within your existing teams. We run pods when you do not. And when the problem is upstream of the hiring, our consultants scope it before anyone is billed.
Founded in St. Louis in 2011, Norton Digital Consulting understands that all successful AI projects start with having the right people in place.
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What AI is already doing
Not pilots. Deployments that large organizations have done successfully. Each one needed the same lifecycle of roles we staff.
Two years in, the assistant still handles about two-thirds of customer inquiries and does the work of more than 850 full-time agents, while every customer keeps the option of reaching a person. Cost per transaction fell 40% over two years.
A GPT-4 assistant lets financial advisors query the firm's internal research in plain language. Adoption sits at 98% of advisor teams, and a sister tool now lets institutional salespeople answer client questions in about a tenth of the time.
An ambient AI scribe drafts the visit note from the conversation, with the patient's consent. After one year and 2.5 million uses across one physician group, it had saved close to 16,000 hours of documentation, and 84% of physicians reported better patient communication.
Inside one of the world's largest engineering organizations, more than 30% of new code is now generated by AI and reviewed by engineers, up from a quarter a year earlier, with leadership citing roughly 10% gains in engineering velocity.
An industrial copilot lets automation engineers generate, explain and debug PLC code in natural language. One automotive supplier uses it on the machines that inspect electric-vehicle batteries, cutting engineering time and the need for specialized programmers.
A retail energy supplier uses generative AI to draft replies to customer emails, with staff reviewing before send. AI-drafted responses reached 85% customer satisfaction against 65% for the human-only baseline.
Results show what is possible when the full lifecycle is staffed.
How we can help
Four pairs that follow the life of a real AI use case. Most failed AI programs staffed only the Build pair. Each role has a defined scope, a vetting rubric and a written plan.
Decide what gets built and why
Owns the use-case portfolio and the value case. Kills the ones that do not pay.
Decides how it gets built: patterns, platform choices, integration approach, standards.
Make it work
Orchestration, RAG, agents, evals, the application layer. Sits with your users.
Pipelines, access to systems of record, permissions. Usually the actual bottleneck.
Keep it working and safe in production
Deployment, observability, model routing, cost, latency, regression detection.
Prompt injection, data exfiltration, agent permission scoping, red-teaming.
Make it legitimate and actually used
Model inventory, policy, privacy and regulatory exposure, audit trail.
Training, champions, workflow change, adoption measurement.
Plus the support roles around them. Delivery and project managers, business analysts, QA and technical writers who have worked on AI programs before, so the eight are not carrying the program alone.
Three ways to work with us
Staff augmentation when you own the roadmap. A led pod when you need the outcome, not the headcount. Consulting when the question is still open. Every model carries the same vetting and the same attention.
Scope an engagementOne engineer or five, on your tools, in your standups, from day one. Contract, contract-to-hire or direct hire.
Best when you already have a technical lead.
A forward-deployed lead with a few specialists who own an outcome: an agent in production, a RAG platform, a voice deployment. Fixed three-week sprints, weekly progress report, priced per pod.
Best when you need the outcome, not the headcount.
A two-week AI Readiness assessment that turns "we should do something with AI" into a scoped build, an eval plan and a budget. Then we staff it, or you do.
Best when the use case is still open.
Our vetting method
of engineers who apply to Norton make it into our ranks. We do not screen for algorithm puzzles. We screen for evidence of AI systems that ran in production, with real users and real results.
Contact usEvery candidate walks us through one AI system they shipped: architecture, what broke, what they would change. We check the story against references.
Cuts about 60% of applicantsA retrieval or agent workflow against a messy real-world dataset, scored with an eval set we provide. We watch how they debug, not just whether it runs.
Cuts about a quarter of the remainderCan they define quality before writing a prompt? Estimate cost at scale? Explain the guardrails they would add for a regulated client?
Cuts about half of the remainderA Norton lead from the target industry runs a working session. Communication with non-engineers is scored, because most of our roles talk to the business every week.
Only the top 4% make it.
Forward-deployed engineering
The role Palantir invented and every enterprise now wants. Our AI Engineers are forward-deployed: they shadow the business process first, put a working prototype in front of real users inside two weeks, and ship version one to a pilot group within the first month.
Demand for this profile is running roughly three times supply in the US. Norton trains for it explicitly, so you are not waiting on a market that cannot deliver.
Talk about an FDEWhere our engineers are deployed
We work where AI has to be right, auditable and in production this quarter. Our leads come from these sectors, and the Gate's final interview is run by one of them.
Client voices
"We had burned six months on a pilot nobody could scale. Norton's architect told us in week one exactly where it would break, and the two engineers who followed had it in production by the end of the quarter. Nobody needed a tutorial on what an eval set was."
The Norton Signal
Original data from our SOWs, playbooks from our pods, and hiring guides for leaders who are not AI engineers themselves.
Demand for forward-deployed AI engineers is running roughly three times supply, and the Run and Adopt roles are not far behind. We pulled rate, tenure and scope data from SOWs to build the first benchmark.
Read the reportFreshness, chunking and access control. The data engineering nobody budgeted for, and how to budget for it.
Read moreSeven questions, what a good answer sounds like, and the two red flags we see most often.
Read moreFor hiring teams
Tell us the role, the stack and the start date. If it is one of our eight roles, or a support role around them, you will have a shortlist in 48 hours. If it is not, we will say so and point you somewhere useful.
Book a scoping callFor AI engineers
No bench-warming, no generic IT tickets. Production AI work with clients who have budget and a decision maker. Take the Gate; we will tell you where you stand either way.
Join the teamContact us
Tell us what you are building and where it is stuck. We will come back within one business day with a straight answer on whether we can help, and who we would put on it.