Bespin builds the full stack beneath your agents, runs it after launch, and holds every agent to the result it was hired to deliver.
Most enterprises have an AI strategy. Far fewer can show what it returned.
A better model ships tomorrow at half the price. When is it in your production?
Fixed at layer 02, Model operations and routing
Who owns each agent you run, and what does each outcome cost?
| Agent | Owner | Cost per outcome |
|---|---|---|
| Claims triage | Unknown | Unknown |
| Invoice match | Unknown | Unknown |
| Support replies | Unknown | Unknown |
| and every agent after that | ||
Fixed at layer 07, outcome management
If either question gave you pause, the gap is in the stack. Find it
Most enterprises built the foundation and jumped straight to agents. The layers they skipped decide whether AI compounds.
Control and outcomes
Business context and agents
Foundation
The people who knew the work chose what to automate and designed the agents themselves.
We manage our agents the way we manage our people: on results.
All 240 worked. None paid for itself, so we retired them.
From a fully private model over 25 million internal records.
Once every agent worked from one shared model of products and regulation.
From an on-premises model that passed a national security certification.
Top-tier on every major cloud.AWS, Google Cloud, and Microsoft Azure, SOC 2 Type II attested.
Bring one workflow that is slow, expensive, or stuck. Our engineers will walk it through the seven layers with your team and scope the build that closes the gap.
Or ask an assistant about us. This site is written for agents too, starting at /llms.txt.
A detection only matters when it reaches the right person with enough context to act. Orbit Vision AI connects the cameras you already have to Bespin’s own detection models, agent triage, and the systems where work gets assigned.
Any vendor can sell a model that spots a defect. Orbit Vision AI is one surface on Bespin’s full stack: the same infrastructure, model routing, and agent workforce we run for the rest of the business, so a detection arrives as a decision, not a frame.
The same dock event from the diagram above, in four frames: what the camera sees, what gets logged, what the plant does on its own, and what a supervisor can still ask.
What the model reads
“A forklift and a pedestrian are both inside the marked walkway, about 1.8 meters apart.”
Forklift 0.97Person 0.99Zone 2
The model writes the scene out in words before anything acts on it. Every step after this reads that sentence, which is why a supervisor can argue with the decision instead of guessing at a score.
Work order raised
Safety response, Dock 4
TriggerProximity breach in the Dock 4 walkway
EvidenceClip and annotated frame attached
SeveritySet from site policy, the shift, and prior events at this bay
Assigned to the dock supervisor, waiting on acknowledgement
Control action dispatched
Beacon and horn, Dock 4
BEACON D4ON
HORN D4ON
CONVEYOR D4RUN
BAY DOOR D4OPEN
Ask the SOP agent
Dock supervisorDock 4 beacon tripped. What do I check before I clear it?
Read from your dock safety procedure
Four systems, one event, nothing re-typed between them. The frames here are drawings of the mechanism, not customer footage.
Four detection patterns cover most of what plants ask us for, and each one runs the same three steps. The classes inside a pattern are trained on your product, your lines, and your rules, so this is the shape of the work rather than a parts catalogue.
Continuous lines, where one grader cannot watch every meter of every shift.
The camera reads
Marks, tears, inclusions, and shade drift, scored against the defect classes your graders already use.
The agent decides
Whether this is one noisy frame or a run forming, by reading the last hour on the same line and the grade history for that product.
The work lands
A graded event on the line dashboard, and a work order once the run crosses the threshold operations set.
Read the patternThe line is running, but not the way it is supposed to run.
The camera reads
Flow, build-up, blockage, alignment, and anything whose normal state is a visual one rather than a sensor reading.
The agent decides
Which station drifted first, and whether what the operator is seeing is a symptom of something upstream of them.
The work lands
An alert at the station, and a control action where a control point is configured for it.
Active floors, loading docks, and anywhere people and machines share a lane.
The camera reads
People, vehicles, and the distance between them inside the zones you mark, plus zone entry and exit. Objects and movement, never identity.
The agent decides
Severity, from site policy, the shift, and prior events at that same location, rather than firing the same alert every time.
The work lands
A beacon or horn at the station, and a safety work order assigned to the supervisor with the clip attached.
Read the patternWhere the count is a clipboard, a spreadsheet, and an argument at the end of the shift.
The camera reads
Pieces, units, pallets, and loads at the moment they pass a camera, per line and per shift.
The agent decides
Where a count diverges from the system of record, by how much, and whether the gap sits inside tolerance.
The work lands
A reconciled count, and an exception raised to an owner when the gap is outside it.
Each frame is a drawing of how that pattern runs, built from the same boxes, labels, and verdicts the operator sees. None of it is customer footage.
Often more than once, and none of it reached the maintenance system. Orbit Vision AI is built to be the last vision system a plant buys.
What most plants have
Maintenance systemNothing arrives. The inspection is still manual.
Orbit Vision AI
Maintenance systemA work order, assigned, with the clip attached.
Orbit Vision AI runs models we train on your lines, defect classes, and safety rules, and we own every step between the camera and the fix.
One team and one platform. No handoffs between vendors. The triage step runs on the same agent workforce behind our agentic AI practice, not a scripted notification rule.
Operations and safety agree the detection accuracy test before anything scales. Each new site is commissioned on its own, not in one cutover.
Runs on Infrastructure at the edge · Model operations and routing · DataOps · Agent build and operations
A detection that stops at a dashboard is a notification. Orbit Vision AI writes into the systems that already run the plant, and it writes the evidence with it.
Maintenance and work orders
A work order in the system that already runs the process, with the clip, the frame, and the zone attached, so nobody re-types the event.
Write
Plant control points
A beacon, a horn, or a stop, dispatched to the control point configured for that station and logged like every other action.
Act
Operator chat and SOP lookup
A supervisor asks what to check and gets the answer out of your own standard operating procedures, with the step and the sign off it needs.
Ask
Dashboards operators open
Per line, per shift, per site. Built to be opened during a shift rather than reviewed after one.
Watch
Alerting and escalation
The channel your team is already in, on the escalation path operations set, not a separate inbox nobody checks.
Notify
Every action is logged with the frame that caused it, so the audit reads the same way the shift did.
A plant floor rules out a round trip to a vendor cloud, on latency and usually on policy too. Orbit Vision AI runs the detection on the floor, and you decide what leaves the site.
On the floor
Edge inference
At the site boundary
What crosses
Your cloud, or ours
History and reasoning
Not every event needs the biggest model, and the biggest model is a different one every quarter. Orbit Vision AI treats model choice as configuration.
Detection model, at the edge
Small, fast, and trained on your classes. Answers almost every event on its own.
Vision language model
Describes a scene when a class on its own does not explain what happened.
Reasoning model
Reads the procedure and answers the operator's question in their own words.
Every task routes to the smallest model that meets the bar, so a better model drops in later without a rebuild.
Operations
“I hear about a defect run after the pallet has already shipped.”
Graded events in the hour they happen, on the line where they happen, and a work order that arrives already assigned.
Plant safety
“Our controls are training, floor tape, and hoping the operator saw the person.”
Zone monitoring on the cameras already covering the docks, with every event logged, owned, and closed out.
Quality
“Two graders disagree and neither of them watched the whole shift.”
One defect class list, applied the same way on every line and every site, with the frame attached to every call.
IT and security
“The last vendor wanted to stream our floor to their cloud.”
Inference on the floor, camera scope agreed with your team, and no facial recognition anywhere in the stack.
Written out the way a buyer reads them: the question we were handed, and how the work gets built.
Continuous defect detection across a multi-site plant network
We have already bought vision more than once. Each system graded one defect on one line, each came from a different vendor, and none of them talk to each other.
Read the patternForklift and pedestrian proximity safety on an active floor
We have had near misses. Our controls are training, floor tape, and hoping the operator saw the person.
Read the patternThe rest of the pattern library, filterable by solution and industry
Vision is one of four. The same question, the build, and the owner, written out for each one.
See all patterns
AI and ML competencies on AWS and Google Cloud, and more than 200 AI projects delivered.
Orbit Vision AI connects computer vision detections to edge deployment, event routing, agent triage, dashboards, and the systems where work gets assigned.
Yes. Orbit Vision AI runs Bespin’s own detection models, trained and tuned on each customer’s lines, defect classes, and safety rules, together with the operational engineering that turns every detection into assigned work.
With one line or one site and a detection accuracy test agreed with operations and safety before anything scales.
No, not to start. We work with the coverage already on site, and any new coverage is scoped against a real gap rather than proposed by default.
No. The detection models classify objects, conditions, and movement inside a zone, not identity. Camera scope and data handling are agreed with your security team before a site goes live.
It reasons. The same agent workforce behind our agentic AI practice applies site policy, shift history, and prior events at that location before it sets severity and assigns an owner, rather than firing the same notification every time.
No. Detection runs at the edge, on the floor. Events, metadata, and the frames you choose to keep are what cross the site boundary, and retention is agreed with your security team before a site goes live.
No. Model choice is configuration. Every task routes to the smallest model that meets the bar, so a better model drops in later without a rebuild.
Start with one line and an accuracy test your operations and safety teams sign off on.
One view of usage, access, policy, and spend across every model, agent, and cloud, with controls sized to the risks you carry.
There are a dozen AI assistants in use, and we cannot say what data has gone into them.
Every new agent and connector is one more access path, and we have no fast way to assess it.
The framework covers everything. We need the part that fits our risk.
AI spend used to be a line item. Now it touches the whole IT budget.
Usage and spend from every gateway, cloud, and device in one place instead of six consoles.
Every call attributed to an application, team, user, and workload, and it stays attributed as new tools arrive.
Budgets, anomaly alerts, and a router that sends each call to the model policy allows.
Right-sized models, caching, leaner context, and commitments priced against real consumption.
Point the same four steps at an agent and you get outcome management, layer 07 of the stack.
Security and finance arrive from opposite directions and want the same record. Whichever calls first, the other follows.
Security asks
What data went where?
Every new agent, assistant, and connector passes the same four checks inside your existing change process. Frameworks like the NIST AI RMF get cut down to the risks you actually carry.
Finance asks
What did it cost, and what did it buy?
Our method for the cost side of AI. The number you manage moves from monthly spend to cost per resolved case.
SOC 2 Type II attested. Our own controls are independently audited too.
Runs on Governance and security · Outcome management · Tokenomics through every layer
When unmanaged AI spend becomes an audit finding
Internal audit asked which AI tools we use, who is paying for them, and what data goes into them. We could not answer any of the three.
Read the patternAI sprawl across a dozen assistants, and no answer for the CISO
There are at least a dozen AI assistants in use across the business. Our data loss tooling is monitor-only.
Read the patternUsage discovery, agent and connector intake, access and approval controls, audit trails, framework alignment, and cost through Tokenomics, across models, agents, assistants, and clouds.
Tokenomics is the cost method inside AI governance. It reconciles usage to actual billing and measures cost per outcome at every layer of the stack.
Bespin builds the governance layer in your environment and can run it for you under Managed AI.
We start with discovery across every gateway, cloud, and card, reconciled to your invoice.
Generative AI that answers with the source attached, and agents that complete the work inside your systems of record. Every answer has a citation. Every consequential action has an approver.
Every month, a senior person keys values from statements into our system by hand.
What we know is spread across documents, recordings, and spreadsheets, and two systems give two answers.
We pay for a stack of SaaS seats and still run the process in spreadsheets.
Each control leaves a line in the trace.
Before the first request
You own the code from day one. Models sit behind a gateway, so moving to a better one is a configuration change and an evaluation run, not a rebuild.
AI competencies on AWS and Google Cloud, and expert teams on Databricks, Anthropic, Snowflake, and OpenAI.
Runs on DataOps · Ontology · Agent build and operations · Governance
With document pre-processing fully automated end to end.
A round-the-clock voice agent takes orders and writes them into the ERP.
From a generative AI advisor running across ten digital channels.
Documents into the system of record, without anyone keying the values
Every month a senior person pulls statements from a dozen portals and keys the values in by hand. It is the most expensive data entry in the company.
Read the patternReplacing a SaaS stack with an agentic deal desk
Quoting a deal touches five systems and three spreadsheets. Every tool we buy to fix it becomes a sixth place to look.
Read the patternFrom enriched data to a qualified pipeline, with a person on every handoff
We have spent real money enriching our data, and our sellers still start every conversation from scratch.
Read the patternDocument automation into systems of record, cited knowledge search, workflow agents, and bespoke agentic applications, each with evaluation, approvals, and a trace of what it did.
Every extraction and answer carries a citation to its source. Where the evidence is missing, the system leaves a blank and routes the item to a person instead of guessing.
Every agent has an owner and a cost and quality measure per outcome, so weak agents get fixed and agents that no longer justify their cost get retired.
We scope one workflow end to end, with the evaluation set agreed before the build.
Managed AI keeps any AI system operational after launch, whether we built it or you did: monitored, supported, re-evaluated as models change, and held to the outcome it was built for.
AI systems rarely fail the way servers do. They drift, multiply, and get more expensive.
Models change underneath you
A provider updates a model and answers shift. We re-run your evaluations, then promote or roll back.
Data and connectors drift
Sources change shape and permissions move. We watch freshness and fix the sync before users notice.
Agents multiply
New agents arrive every month. Each gets intake, an owner, and a seat in the next portfolio review.
Costs creep
Tokenomics watches spend per agent and per outcome, and alerts on the spike, not the invoice.
We work inside your change, approval, and security processes, so a problem has one owner instead of a chain of vendors.
One ownerBespin runs all five, inside your processesSet per system, and extended as the system moves closer to customers and revenue.
Runs across every layer of the stack · Tokenomics watches the cost of each
Every review covers each agent’s owner, results, and cost per outcome, and ends in expand, fix, or retire. We run our own agents the same way.
Bespin’s own portfolio, 751 agents designed
Keeping an enterprise AI platform healthy after launch
We launched an AI platform, and adoption outran the team that built it.
Read the patternMonitoring, incident response, model and prompt releases, re-evaluation as models and data change, connector and permission upkeep, cost control through Tokenomics, and regular agent portfolio reviews.
Yes. A system we did not build goes through an intake review first, covering ownership, access, evaluation, and rollback, and then runs on the same terms as the ones we did.
Per system. Most start with business-hours support and extend toward around-the-clock coverage as they move closer to customers and revenue.
We take any AI system through intake and run it on the same terms as our own.
Seven layers, from where a workload runs to whether each agent pays for itself. We use it to find the gap, build only what is missing, and run what we build.
They answer the foundation questions with confidence. Walk all seven with your team, and open any layer to see what we build there.
Control and outcomesBusiness leaders start here
Business context and agentsData and AI teams start here
FoundationCIOs and platform teams start here
Across every layer
The bands are also the build order: design for value at the foundation, engineer for production in the middle, operate and scale at the top. Tokenomics and Managed AI run through all three.
Teams build the foundation and jump straight to agents. The layer underneath every agent and the layer above it decide whether AI compounds.
Layer 04 · Ontology
Without a shared model, each agent invents its own customer, order, and claim, and a year in there are twenty pilots that do not add up to anything.
Without one
customer = policyholdercustomer = account numbercustomer = whoever calledWith one
13%+higher answer accuracy in financial services, once every agent worked from one shared model. Your tenth agent costs less than your first.
Layer 07 · Outcome management
The top layer answers the question a CFO asks: what did this produce, and was it worth it. Agent workforce management treats agents like staff. Scale what earns its keep, fix what falls short, and retire the rest.
Every agent gets
Bespin’s own 751 agents
The engine underneath: AI governance and TokenomicsForward-deployed engineers work inside your repositories, cloud accounts, and systems of record. The people who scope the work build it, which is what gets an AI system through security review.
Walk the seven questions with your team and find the two or three layers where the answers run out.
Cut one business process through the missing layers, with an acceptance test agreed before anyone writes code.
Managed AI keeps it healthy, and the next process reuses the layers already built, so each one costs less than the last.
Every engagement is scoped, staffed, and priced against an outcome you can verify.
Our engineers walk the seven questions with your team and show where the answers run out.
Results from our own operations and from customer systems in production, and the patterns we build most.
Bespin is customer zero. The people who knew the work chose what to automate and designed the agents themselves.
We manage our agents the way we manage our people: on results.
Built by the teams who do the work, not handed down by a central group.
From the agents that stayed in production.
All 240 worked. None paid for itself, so we retired them.
We rebuilt parts of our own deal process as an agentic application, in place of a stack of SaaS subscriptions and spreadsheets. The workflow shapes the software, and agents do the work that used to be a seat in someone else's product.
From a fully private model over 25 million internal records.
Once every agent worked from one shared model of products and regulation.
A round-the-clock voice agent takes orders and writes them into the ERP.
From an on-premises model that passed a national security certification.
With document pre-processing fully automated end to end.
From a generative AI advisor running across ten digital channels.
Partner tier
Competencies
Awards and security
Expert teams on
Documents into the system of record, without anyone keying the values
Every month a senior person pulls statements from a dozen portals and keys the values in by hand. It is the most expensive data entry in the company.
Read the patternReplacing a SaaS stack with an agentic deal desk
Quoting a deal touches five systems and three spreadsheets. Every tool we buy to fix it becomes a sixth place to look.
Read the patternFrom enriched data to a qualified pipeline, with a person on every handoff
We have spent real money enriching our data, and our sellers still start every conversation from scratch.
Read the patternYears of expertise, searchable with the source attached
A decade of what we know is spread across documents, recordings, and spreadsheets, and two systems give two answers to the same question.
Read the patternA multi-agent assistant over nationwide public records
The answers we need sit in public records across every jurisdiction, in a different format in each one.
Read the patternGiving teams the standards their coding agents will actually follow
Our developers are shipping AI-generated code faster than we can review it, and our standards live in a wiki no agent has ever read.
Read the patternWhen unmanaged AI spend becomes an audit finding
Internal audit asked which AI tools we use, who is paying for them, and what data goes into them. We could not answer any of the three.
Read the patternAI sprawl across a dozen assistants, and no answer for the CISO
There are at least a dozen AI assistants in use across the business. Our data loss tooling is monitor-only.
Read the patternKeeping an enterprise AI platform healthy after launch
We launched an AI platform, and adoption outran the team that built it.
Read the patternContinuous defect detection across a multi-site plant network
We have already bought vision more than once. Each system graded one defect on one line, each came from a different vendor, and none of them talk to each other.
Read the patternForklift and pedestrian proximity safety on an active floor
We have had near misses. Our controls are training, floor tape, and hoping the operator saw the person.
Read the patternNo pattern matches both filters yet.
Start with one workflow. We will show where it stalls and scope the build that fixes it.