For mid-market leaders and enterprise divisions

AI that does real work inside your business.

An AI Pod is a small team of our experts working with AI agents, embedded in your business to take on real work. Month-to-month, using the tools you already have.

Talk to us about a pod →One-page overview

Trusted by teams we've served over 20 years

Years of software delivery: 20+; Years of software delivery
Clients served: 300+; Clients served
To launch a new insurance product line with a pod: ~8 days; To launch a new insurance product line with a pod

The shift

The licenses are bought. The work hasn't changed.

Why now

Your peers are moving from AI tools to agents doing the work, and most experiments never reach day-to-day operations.

Trust is the blocker

AI that nobody checks is AI nobody trusts with the work that matters.

The old model breaks

When agents do the work, value no longer comes from billable hours or added headcount.

What a pod is

AI Pods: experts and AI agents, working as one team.

Who is on it

2 to 4 senior people: an AI engineer or developer and a technical lead who knows your industry, with a domain specialist from size M.

What it does

The team works with a fleet of AI agents inside your business. The agents do the work, and our people direct it and check it.

How you engage

Month-to-month, or an engagement of about six months. Your team stays as involved as it wants to be. No big up-front commitment.

Your tools

We use the tools you already have, or recommend ones that fit. You are never locked in to one vendor.

Small on purpose

A good engineer working with AI can do the work of five to ten people. So a pod stays small, and you are not paying for a crowd.

Inside your team

Not a project thrown over the wall: a small team working inside yours day to day, learning your business. Assign someone of yours to work with the pod, and the skill grows in-house.

Boutique, not big consultancy

Senior people you can get on the phone, on a month-to-month basis. A pod costs far less than a big-consultancy engagement, with no large team billed by the hour.

Agents do the workExperts check itYou own the outputMonth-to-monthNo vendor lock-in

Who it's for

Mid-market companies

About 50 to 500 people: big enough to have work piling up, small enough that a pod changes how the business runs.

A division of a larger enterprise

One department or business unit with a clear owner, a budget and a process worth changing, without a company-wide program.

Leaders past the first licenses

Teams that have Copilot, ChatGPT Enterprise or Claude in place and now want AI doing the work, not another pilot that stalls.

Proof

Pods at work.

InsuranceStart-up carrier

Launching new product lines in days, not months.

~8 days

to launch a new insurance product line (it varies by line), where it used to take months

The problem

Every insurance product starts as industry-standard ISO forms: hundreds of pages of coverage questions, rate structures, premium calculations and underwriting rules. Keying them into a policy-administration system by hand takes months per line.

What the pod did

A pod embedded with a new carrier as its AI engine: AI strategy, software, and agents that read the ISO forms, map every element into the digital policy-administration platform, and check each output against the source before pod experts sign off.

How the pod did it

The problem

What the pod did

Outcome

New product lines live in about 8 days
One repeatable process for every new line
ISO structure preserved for state filings

SoftwareAI infrastructure product

A vibecoded prototype, industrialized into continuously verified production.

1,100+

work units completed, each built to a written, locked spec and verified before it landed

The problem

AI makes a working prototype easy. Production is another matter: no specification, no tests, and no record of why the code does what it does. That is where most AI-built software stalls.

What the pod did

A pod running Veracity's agentic software factory. Every change starts from a written, locked spec; AI agents build in parallel, each in its own workspace; automated verification and an engineer's review come before anything lands.

How the pod did it

The problem

AI makes a working prototype easy. Production is another matter: no specification, no tests, and no record of why the code does what it does. That is where most AI-built software stalls.

What the pod did

Outcome

Speed: agents work in parallel on spec-sized units
Auditability: every line traces back to its spec

InfrastructurePortable compute

AI models hosted and run on a data-center company's own compute.

Models on the client's own hardware

with the operating setup to keep them running, and no single-cloud dependence

The problem

A company building portable data-center compute needed AI models running on its own hardware, plus the operational setup to deploy, monitor and update them, without handing the work to a hyperscaler.

What the pod did

A pod set up model hosting on the client's compute and built the operating setup around it, working inside the client's team so its own people can run it day to day.

How the pod did it

The problem

A company building portable data-center compute needed AI models running on its own hardware, plus the operational setup to deploy, monitor and update them, without handing the work to a hyperscaler.

What the pod did

A pod set up model hosting on the client's compute and built the operating setup around it, working inside the client's team so its own people can run it day to day.

Outcome

Models running on the client's own hardware
An operating setup the client's team runs
No dependence on a single cloud vendor

Business operationsAdministration

An agentic chief of staff that manages administrative tasks within a business.

Admin tasks taken on, not just listed

a daily action brief that takes on tasks itself, helps people finish theirs, and sends nudges to keep work moving

The problem

Administration spread across several mailboxes, calendars and business entities. Commitments live in inboxes, follow-ups slip, and hours go to triage instead of the work.

What the pod did

A pod built an agentic chief of staff: AI agents triage email across mailboxes and entities, manage the calendar and to-dos, take on admin tasks themselves, help people complete the ones still open, and send nudges so nothing stalls.

How the pod did it

The problem

Administration spread across several mailboxes, calendars and business entities. Commitments live in inboxes, follow-ups slip, and hours go to triage instead of the work.

What the pod did

Outcome

Tasks taken on and moved forward, not just listed
Nudges that keep open work moving to done
A person approves every outbound message

LegalHistorical records

Legal document transcription and collection for historical record purposes.

A paper archive, now searchable and queryable

a large collection of handwritten and legal documents, transcribed, viewable and preserved

The problem

A large collection of handwritten and legal documents existed only on paper and in scans: slow to read, impossible to search, and at risk of being lost.

What the pod did

A pod built a transcription pipeline in which AI reads each handwritten and typed document, organized the results into a structured collection, and published it as a private archive site.

How the pod did it

The problem

A large collection of handwritten and legal documents existed only on paper and in scans: slow to read, impossible to search, and at risk of being lost.

What the pod did

A pod built a transcription pipeline in which AI reads each handwritten and typed document, organized the results into a structured collection, and published it as a private archive site.

Outcome

Transcribed documents you can view, search and query
Organized into a structured collection
Preserved in a private archive site

Results on our platform

Average ROI: 789%; Average ROI
Annual savings: $500K+; Annual savings
Recovered per week: 700 hrs; Recovered per week
Saved per project: 1,100 hrs; Saved per project

Financial Services

Revenue and EBITDA extraction from tax documents and P&Ls for SBA 7(a) lending

Faster, more accurate loan valuations.

Healthcare

AI staff scheduling across 6 regions and 3 shifts

Overtime cut, staffing gaps eliminated.

Legal

Mediation documents generated from live case data

99% document accuracy.

Manufacturing

Competitive head-to-head product reports for a Fortune 500 manufacturer

257 hours saved in 10 weeks.

Consulting

AI business diagnostics

Weeks of analysis compressed to minutes.

Construction

Generative design and automated code-compliance for architecture and construction

Fewer errors, fewer delays.

Talk to us about a pod →Results by industry

Governed by design

AI you can put in front of your board.

The question executives ask first is whether they can trust it. This is how a pod earns that.

People where it matters

Agents carry the routine work. Your people and ours sign off at the decisions that count, not on every step, so nobody becomes a rubber stamp and nothing ships unchecked.

The plan and the design, before agents start building
Anything that changes a customer, a record or money
Every release, after automated tests and an engineer's review
Anything sent outside your business

Agents that build on each other

One governed set of agents connected to the stack you already run (ticketing, wiki, data platform, documents and models), sharing context instead of a scatter of one-off bots. Governance grows as the number of agents does.

Built for your CIO and CISO

Work happens in your environment and your tools. Every agent action leaves an audit trail, access follows your rules, and you own the output and the code.

Delivered on our SOC 2 Type II and HIPAA-compliant platform, with the best model for each task across Anthropic, OpenAI and 150+ more.

SOC 2 Type IIHIPAA compliant150+ models

How it works

Decide with evidence. No big-bang commitment.

Outside-in view

We come prepared

We start with your industry, not a blank page: where peers are moving, who is leading and who is lagging, and two or three places a pod could pay off in your business. Then we agree one outcome, the one with the clearest measurable benefit: a process where work is piling up, or a prototype that needs to reach production.

Pilot

Prove it

A small pod embeds with your team on that one job, typically for about six months or month-to-month. We measure speed, quality and cost against how it's done today. It can be experimental too: if you think you know where to go, the pod helps you vet it and get there.

Scale

Expand on results

Keep what works, trim what doesn't, and extend the pod to other parts of the business on the evidence. No big up-front commitment.

Talk to us about a pod →

Pods and sizes

One pod model, configured for the job.

Every pod has the same shape: a small team plus AI agents. What changes is who is on it and what it works on. Here are some of the ways we configure pods, each set up differently for its client.

Agentic Operations

Run a live business process from start to finish, with AI agents doing the work and a person signing off.

See the case

Agentic Product

Build AI-native products & copilots — new, or embedded into the apps you already run.

AI Engineering Enablement

Adopt AI coding tools, techniques, and workflows across your engineering teams — with standards and guardrails.

Vibe-to-Enterprise

Turn AI-built prototypes into tested, reliable production products.

See the case

AI Modernization

Move older systems onto modern platforms and get them ready for AI to work with.

Always-on AI Assistant

An always-on AI agent, built on the open-source Hermes Agent, that handles recurring work and messages for you. We deploy it, run it, build it out, and maintain it.

Pod sizes

Sized to the job, and kept small on purpose.

Every pod covers the same three jobs: building with agents, reviewing what they produce, and owning the business outcome. A larger pod adds people and parallel work, not layers.

SFocused

Two senior people plus agents: an AI engineer who builds and runs the agents, and a technical lead who knows your industry, owns the outcome with you and reviews every release.

One workflow or one agent: an always-on assistant, AI coding adoption for a team, a small operations process.

MProduct

Three senior people plus agents: the AI engineer and technical lead, joined by a domain specialist who knows how the work is done in your industry.

An AI-native product or copilot, a prototype taken to production, a mid-size operations process.

LProgram

Four senior people plus agents: the AI engineer and domain specialist, with an architect as lead and a full-stack engineer, so two streams of work run in parallel.

Modernizing a core system, or several connected workflows across a division.

Token-metered. Each proposal states the monthly pod fee and an estimate of model usage.

Why Veracity

We don't hand you agents. We deliver results you can measure.

Veracity has delivered software for more than 20 years. AI Pods are how we deliver it now.

How a pod differs

Team

Veracity AI Pod2 to 4 senior people

Big consultancy: Large, billed by the hour
Staff augmentation: People you manage
AI tool vendor: None: you get software

Who does the work

Veracity AI PodAI agents, directed and checked by experts

Big consultancy: Consultants
Staff augmentation: Contractors
AI tool vendor: Your team, if it can

Governance

Veracity AI PodCheckpoints and audit trail built in

Big consultancy: Reports and steering committees
Staff augmentation: Yours to provide
AI tool vendor: The vendor's defaults

Commitment

Veracity AI PodMonth-to-month or about six months

Big consultancy: Long statements of work
Staff augmentation: Per head, per hour
AI tool vendor: Seats and licenses

Lock-in

Veracity AI PodNone: your tools, your output

Big consultancy: Their methods
Staff augmentation: Their people
AI tool vendor: Their platform

	Big consultancy	Staff augmentation	AI tool vendor	Veracity AI Pod
Team	Large, billed by the hour	People you manage	None: you get software	2 to 4 senior people
Who does the work	Consultants	Contractors	Your team, if it can	AI agents, directed and checked by experts
Governance	Reports and steering committees	Yours to provide	The vendor's defaults	Checkpoints and audit trail built in
Commitment	Long statements of work	Per head, per hour	Seats and licenses	Month-to-month or about six months
Lock-in	Their methods	Their people	Their platform	None: your tools, your output

Senior-led

US experts own the outcome and acceptance, not offshore ticket queues.

Proven discipline

A spec-driven, continuously-verified delivery method, not slideware.

Experience where it counts

Two decades in financial services, insurance and healthcare, where mistakes cost the most.

Start with a conversation.

Tell us your company and the outcome you care about. For a qualified inquiry we prepare a one-page outside-in view of your industry and where a pod could pay off, then tell you plainly whether a pod fits and what it would take on first.

Talk to us about a pod →One-page overview

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