Invoice exceptions and accounts payable
Three way match, duplicate and overbilling detection, exception routing with the evidence attached. Cleared a 12,000 invoice backlog for a 3PL and recovered $1.4M. Invoice exception handler.
Award-Winning AI Automation
The Back Office Work We Take Off Your Team
Not chat. Not a copilot that suggests. Agents that read the document, check the system, make the routine decision, and hand the exceptions to a person with the reasoning attached. Eight of the 32 we run in production:
Reconciliation and cash application
Matches remittances to open items across formats, posts the clean ones, and explains the rest. Reconciliation agent and cash application agent.
Prior authorization and eligibility
Checks coverage, assembles clinical evidence, submits, tracks and escalates. Turnaround from six days to under 24 hours in one deployment. Prior authorization agent, eligibility verification agent.
RFP and proposal drafting
Reads the RFP, pulls approved answers from your library, drafts the response and flags what has no precedent. RFP response agent.
Contract clause review
Compares incoming contracts to your playbook, marks deviations by risk, and drafts the redline for a lawyer to approve. Contract clause review agent.
Vendor risk and spend
Continuous vendor monitoring, spend categorisation and requisition advice inside the procurement flow you already run. Vendor risk monitor, spend categorisation.
Renewal risk and pipeline hygiene
Flags accounts likely to churn 90 days earlier and keeps CRM data honest without asking reps to do it. 42% less gross churn at a SaaS company. Renewal risk agent.
Patient and customer outreach by voice
Answers inbound calls, books, confirms and recalls. A specialty network went from 61% to 94% of calls answered. AI voice agent.
Advice Is Cheap. Here Is What Went to Production.
Each number is from a system we assessed, built and shipped. Each card links to the full case study.
See all case studies$1.4M
Overbilling recovered by an accounts payable agent that cleared a 12,000 invoice backlog for a 3PL, with 80% less time spent on exception handling.
80%
Cycle time reduction on prior authorization with AI agents, 65% less clinical staff time per request and a 79% first pass approval rate.
42%
Less gross churn for a SaaS company after a renewal risk agent started flagging accounts 90 days earlier, lifting net revenue retention to 118%.
60 to 15 days
Average reimbursement cycle after automated billing and coding, with a 6% claim rejection rate and $150K recovered from denials.
20 hrs to 1
Weekly insurance verification call time cut to under an hour, 12 seconds per patient, and $87K a year in prevented eligibility denials.
How We Automate a Process Without Breaking It
Four steps, each with a decision point. Hover or tap a step to see what happens in it.
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Step 1: Map the process as it actually runs
Step 1: Map the process as it actually runs
We sit with the people who do the work, not just their managers, and document the real flow: the inputs, the systems touched, the decisions made, the exceptions, and how often each one occurs. This is where most automation projects go wrong, because the documented process and the actual process are rarely the same thing.
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Step 2: Decide what the agent may decide
Step 2: Decide what the agent may decide
Every decision in the flow gets a confidence rule: the agent acts alone above it, a person reviews below it, and some decisions are never automated at all. Those thresholds are written down and approved by the process owner before a line of code exists, and they are what makes the system safe to run in a regulated operation.
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Step 3: Build, then run it in shadow
Step 3: Build, then run it in shadow
The agent connects to your real systems and processes real volume, but a person still makes every decision. We compare its choices to theirs for two to four weeks, tune, and only then let it act alone within its approved thresholds. You see the accuracy numbers before anything is at stake.
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Step 4: Run, measure, widen the lane
Step 4: Run, measure, widen the lane
In production, every action is logged with its reasoning, exceptions go to a named queue, and a weekly report shows volume handled, accuracy, and the money and hours it returned. As confidence holds, thresholds move and the agent takes on more. Your team is trained to own the thresholds, so the system keeps improving after we step back.
What You Are Really Choosing Between When You Automate
AI automation is sold by very different kinds of provider using very different tools. Here is what each one is genuinely good for, and where it stops.
Traditional RPA
Right for stable screens and fixed rules
UiPath and Power Automate style bots follow a recorded script. Excellent when the input is structured and the process never changes. They break when a form moves, cannot read a messy PDF, and make no judgment at all. Many organisations already own one and are not using it.
No code automation tools
Right for glue between SaaS apps
Zapier, Make and n8n connect one app to another and can call a model in the middle. Fast to start and cheap at low volume. They are not built for a process that touches a system of record, needs an audit trail, handles regulated data, or has to run reliably at ten thousand transactions a month.
The typical AI automation agency
Right for a marketing workflow, risky for finance
Most agencies on this search are small teams wiring no code tools together for lead follow up and content. Some are very good at that. Few have shipped anything into an accounts payable ledger, a clinical system or a claims platform, or carry the compliance obligations that come with doing so.
Build in house
Right after the first one has shipped
The end state we work toward with every client. The risk is starting here: a team learning production AI on a live financial or clinical process without having seen what goes wrong. Bring that experience in for the first agent, then own the rest.
Bonami
Engineered agents inside your systems of record
32 agents in production across finance, procurement, sales, IT and clinical operations. Custom built against your APIs, with confidence thresholds the process owner approves, a shadow run before anything acts alone, every action logged with its reasoning, and delivery to HIPAA and SOC 2 Type II controls. You own the code.
When not to automate
What we will tell you if it is true
If the process is too inconsistent to define thresholds for, if volume is too low to justify the build, or if a rules engine would do the job at a tenth of the cost, we say so in the assessment. An automation that has to be babysat is worse than the manual process it replaced.
The Stack We Recommend Because We Run It
Vendor neutral by design. We recommend what fits your data, your cloud and your compliance obligations, then build on it. The models are the cheap part; the retrieval, evaluation, human review and audit layers around them are where the engineering lives.
Models & Platforms
Frontier and open weight models chosen per use case on quality, latency, cost per call and data residency. Never a single vendor bet.
- OpenAI & Azure OpenAI
- Anthropic Claude
- Google Gemini & Vertex AI
- Open weight models (Llama, Mistral)
- PyTorch
- TensorFlow
Agents, Retrieval & Orchestration
How a model becomes a system that does work: grounded in your documents, able to call your APIs, and stopped by a person when confidence is low.
- Agent orchestration (LangGraph, custom)
- Retrieval and vector search
- Tool calling into systems of record
- Evaluation harness per use case
- Human in the loop review gates
- Python
Cloud & Data Infrastructure
Built inside the cloud you already run, defined in code so environments are reproducible and auditable.
- AWS
- Microsoft Azure
- Google Cloud
- Kubernetes
- Docker
- Terraform
- PostgreSQL
- Data pipelines and warehouses
Governance, Security & Observability
What lets a regulated organisation run this in production: every prompt and output logged, secrets managed, access controlled, drift watched.
- Datadog
- Grafana
- Prometheus
- HashiCorp Vault
- Okta
- Immutable prompt and output logs
- PII and PHI redaction
If your organisation has standardised on a platform, we build inside it. The assessment maps our recommendations to what you already license before anything new is proposed.
By Industry: Where the Work Actually Lands
The same discipline, applied to the processes each sector repeats at scale.
Prior authorization and eligibility agents
Ambient clinical documentation
Denial management and underpayment recovery
Patient outreach voice agents
HIPAA and HITECH governance built in
Reconciliation and cash application
Invoice exception handling
Claims and underwriting document review
Model risk management and audit trails
Accounts payable and carrier invoice audit
Vendor risk monitoring
Spend categorisation
Purchase requisition advisory
Renewal risk and churn agents
Deal forecasting and pipeline hygiene
RFP response drafting
AI features inside your own product
Contract clause review
Document discovery and comparison
Intake and matter routing
Confidentiality and retention controls
Award-Winning AI Development & Consulting
100 Fastest Growth Companies
Global Spring Winner
Top App Development Company
AWS Partner Network
Google Cloud Partner
Highly Rated on Trustpilot
Verified Agency
Top App Development Company
ASSOCHAM Member
Frequently Asked Questions
[ 1 ]
What does an AI automation agency do?
It takes a business process that people currently run by hand, usually one involving reading documents, checking systems and making routine decisions, and builds software agents that do that work, escalating the exceptions to a person. The useful distinction is between agencies that wire no code tools together for marketing and sales flows, and engineering teams that build agents into systems of record like an ERP, a claims platform or a clinical system. We are the second kind.
[ 2 ]
How is AI automation different from RPA?
Robotic process automation follows a recorded script against a fixed screen. It is excellent for stable, rule based tasks and breaks the moment a form changes or a document is unstructured. AI automation reads the document, understands intent, checks the relevant system and makes a judgment within approved limits. In practice the two often work together: the agent decides, and an existing RPA bot or API call carries out the action.
[ 3 ]
What processes are worth automating first?
The ones where judgment is repeated at high volume against documents and systems: invoice exceptions and three way matching, reconciliation and cash application, prior authorization and insurance eligibility, contract clause review, RFP drafting, vendor risk monitoring, renewal risk, and inbound call handling. All eight are agents we already run in production. The assessment scores your candidates on volume, consistency, value and data readiness, and the first one is usually obvious once those are on one page.
[ 4 ]
How do you keep an AI agent from making a costly mistake?
Every decision in the process gets a confidence threshold the process owner approves in writing: act alone above it, route to a person below it, never automate certain decisions at all. Before go live the agent runs in shadow for two to four weeks, making every decision while a person still executes, so you see its accuracy against theirs with nothing at stake. In production every action is logged with its reasoning, and exceptions go to a named queue. Thresholds only widen as measured accuracy holds.
[ 5 ]
How long does it take to get an agent into production?
Process mapping and threshold design take two to three weeks. Build and integration four to eight depending on the systems involved. Shadow run two to four. So a first agent typically acts alone in production ten to fourteen weeks after kickoff, with a measured accuracy record before it does. Additional agents on the same systems are faster because the integration work is already done.
[ 6 ]
How much does AI automation cost?
The assessment is a fixed fee. The build is quoted as a fixed price after it, because by then the process, the systems and the integrations are known, and it runs from the tens of thousands of dollars for a single agent on well documented APIs upward with integration count and data complexity. Ongoing cost is a monthly figure for hosting, model usage, monitoring and support, not a per transaction fee that grows with volume. The business case is built on your own volumes and labour costs before you commit to anything.
[ 7 ]
Do you use Zapier, Make or n8n?
Not for anything that touches a system of record, handles regulated data, or needs an audit trail. Those tools are excellent glue between SaaS applications at low volume and we will say so when they are the right answer. For finance, procurement, clinical and claims processes we build engineered agents against your APIs, in your cloud, with logging and access controls a regulated organisation can pass an audit with.
[ 8 ]
Will it work with our ERP, CRM or clinical systems?
Yes. Agents integrate through the systems' APIs, and where an API does not exist we scope the connector first. We have integrated with major ERPs, CRMs, practice management systems and EHRs including Epic, Oracle Health and athenahealth. The assessment includes a systems inventory precisely so the integration cost is known before the build is quoted.
[ 9 ]
How do you handle security and compliance?
Agents run in your cloud or ours under your controls, with role based access, secrets management, and sensitive data redacted before it reaches a model where the use case allows. Every prompt, output and action is logged immutably. In healthcare we work under a Business Associate Agreement to HIPAA and HITECH; across sectors we deliver to SOC 2 Type II controls. Your data is never used to train models.
[ 10 ]
What happens after go live?
A weekly report shows volume handled, accuracy against the thresholds, exceptions raised, and the hours and money returned. Model drift and integration failures are monitored and alert us before they reach your queue. Your team is trained to own the thresholds and the exception queue, so the system improves after we step back. Support continues on a monthly agreement for as long as you want it, and you own the code either way.
Pick the Process That Costs You the Most Hours Every Week
Bring it to a thirty minute call. We will tell you whether it can be automated safely, what the agent could decide alone, what it would hand to a person, and roughly what it would return. If it is not worth automating, you will hear that too. Not sure where to start? Begin with an AI readiness assessment, or see the full catalogue at Bonami X-AI.
Book an Automation Assessment40%
Average Productivity Increase
60%
Reduction in Manual Tasks
96%
Client Satisfaction Rate
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