Back-office AI agent development companies

Back-office work is the internal work that keeps a company running and that customers never see. It includes checking supplier invoices against orders, handling insurance or warranty claims, reading contracts for risky terms, filling in records and writing reports. An AI agent for this work reads a document or a request, looks up the data it needs in the company's systems, does the next step, and passes anything unclear to a person. The steps are chosen by the AI model as it goes, inside limits the company sets.

This page lists the firms in this guide that say they build back-office agents. Reveneau and Fluxon come first, and the other fourteen follow in the guide's order, which gives no ranking. Every result on this page is the firm's own, and no outside source checked any of them. Anthropic's partner directory, Anthropic's list of service firms that work with its Claude models, gives most firms a tier, set by Anthropic: Global Premier, Preferred or Select. The full list of AI agent development companies has each firm's office, size and tier in Anthropic's partner directory.

Why a person should approve a back-office agent's work

Anthropic, which makes the Claude models, writes in its guide "Building effective agents" that "The autonomous nature of agents means higher costs, and the potential for compounding errors." [1] Compounding errors are mistakes that build on each other from one step to the next. Anthropic recommends "extensive testing in sandboxed environments, along with the appropriate guardrails". Sandboxed environments are test copies of a company's systems where a mistake harms nothing, and guardrails are rules that limit what the agent may do. That is the model maker's own advice. Several firms below describe how a person approves the agent's work, and that description is worth asking for in writing.

Reveneau and Fluxon

Reveneau offers a service to build workflow automation, the software that moves work from one system to the next, and a service for AI development. By its own account, AI writes 100 percent of its code. Reveneau says every change must pass a large eval suite, a set of automated tests written from the project's specification, before it is released, and that Jev, the model that grades the suite, makes it run ten times faster than its previous grader. It says it uses AI in place of hiring more engineers and puts that saving into the prices clients pay. Reveneau says it takes responsibility for the whole project through production, the live system people use, and after release. Its offices are in San Francisco and Vancouver, and Anthropic's directory had no Reveneau entry when its 321 entries were searched on 20 October 2026.

Fluxon's enterprise page tells buyers to "Connect trusted data and business logic so agents can reason accurately and act reliably." [13] Business logic means the rules a company follows, such as who may approve a payment. No back-office agent case for a named client was found on Fluxon's pages, and Anthropic's directory had no Fluxon entry on 21 October 2026.

The fourteen firms at a glance

Firm	Back-office work it names	Who approves, by its own pages	Result it publishes, its own
BotsCrew	Internal assistant for support staff	Not described	See the customer support page
Galadrim	File checks, contracts, investment memos	Not described; it tests against reference cases	"4 operational AI agents in 6 months"
Thinkia	Business-critical processes	Not described	None found
Lazer Technologies	Routing, approvals, compliance drafts, document review	Not described	None found
Mactores	Claims, on a platform it says meets HIPAA	Its engineers set "what humans must approve"	"12 weeks to production" for a claims system
Yellow Tech	Delivery notes, orders, invoices, product sheets	"Exceptions go to human review"	A time saving for Nital, no method shown
Linnify	Invoices, contracts, forms	Not described	">95% average accuracy", no method shown
Awiant	Supplier invoices	"approved by a person"	Up to 20 minutes to about five, customer estimate
Cay Digital	Finance and operations	"a person approves, with an SLA"	None for back office
Zemoso Labs	Jobs scored before any agent is built	An "oversight screen" designed first	None found
Diverger	Office work through Gemini Enterprise	Not described	"+40%", source not stated
HAVIT	Agents that read and act inside client systems	Not described for back-office work	Figures for one code case
Bounteous	Agentic workflows on its own platform	"human oversight"	None found
First Line Software	Invoicing, reconciliation, month-end accounts	Not described	None for back office

The firms

BotsCrew. Its about page says it builds "AI agents and automation that remove busywork" [14]. Its S&B Filters case includes an internal assistant that support staff use to answer order questions, connected to NetSuite, the client's system for orders and accounts. The figures BotsCrew publishes for S&B Filters are given on the customer support page of this guide. Anthropic lists BotsCrew at the Select tier.

Galadrim. Its agents page describes Athos, built for Effy, as "an AI-First platform based on an agent system combining OCR and LLM: it classifies files, verifies their regulatory compliance, and isolates anomalies for instructors." [3] OCR is software that reads text from scanned pages, and an LLM is a large language model. For Mercialys, a client it names, Galadrim describes four agent systems built in six months: "contract analysis, internal chatbot, investment memo generation, and automated monitoring". Its own result line for that case is "4 operational AI agents in 6 months". Anthropic lists Galadrim at the Select tier.

Thinkia. Its homepage says, "We put AI agents to work running your business-critical processes" [16]. It sells its own platforms beside its build work, so some of its agent work may be a licence for one of its products. No named client case was found on the pages read. Anthropic lists Thinkia at the Select tier.

Lazer Technologies. Its AI page lists "Internal ops: routing, approvals, compliance drafting, document review" [10]. Routing means sending each request to the right team, and compliance drafting means writing first drafts of documents that rules require. The page lists the work and no results. Outside Anthropic's Select tier, these facts come from Lazer's own pages.

Mactores. Its AI agents page says, "Mactores delivered a HIPAA-compliant agentic AI platform for KlearTrust." [9] HIPAA is the US law on keeping health records private, and an agentic platform is a system built on agents. The case heading reads "KlearTrust: Six-month claims cycle replaced in twelve weeks". The same page gives "Before: Six-month manual claims-review cycle" and "After: Twelve-week production HIPAA-compliant agentic claims platform", and Mactores' homepage lists the case with "12 weeks to production" as its outcome [18]. So the twelve weeks is the time Mactores gives for building the system. Neither page says how long a claims review takes now, so ask Mactores for that figure. Mactores says its own platform, Aedeon, "builds the agent infrastructure". Outside Anthropic's Preferred tier, these facts come from Mactores' own pages.

Yellow Tech. Its AI Factory page describes document agents: "They receive documents of every kind, such as delivery notes, orders, invoices and product sheets, extract the data, validate it against a confidence threshold and write it to the ERP. Exceptions go to human review." [4] An ERP is the system that runs a company's orders, stock and accounts, and a confidence threshold is a minimum score the extracted data must reach. Its case page for Nital says, "Yellow Tech built an agent with Nital's IT team that generates the product sheets." [5] The same page reports a person at Nital pointing to accounting, "where reconciling courier invoices used to take a full working day and now takes a few minutes". It does not say how that was measured or whether an agent does the work. Anthropic lists Yellow Tech at the Select tier.

Linnify. Its AI page offers "AI agents that extract, understand, and validate data from documents such as invoices, contracts, and forms", and says it connects agents to "ERP, CRM, product platforms, and internal tools" [8]. A CRM is the system that holds customer records. The page states ">95% average accuracy across agents deployed in production", with no definition of accuracy and no test set. Outside Anthropic's Select tier, these facts come from Linnify's own pages.

Awiant. Its case study for Volkswagen Financial Services describes agents for one category of supplier invoices, which must be matched to purchase orders and checked against price lists, agreements and service records [2]. The agent recommends accepting or rejecting each invoice and gives its reasons. The case page says, "Handling time for these invoices went from up to 20 minutes to about five minutes each, based on the customer's own estimates." Each invoice is "approved by a person", and "only invoices meeting predefined criteria will move toward autonomous handling as the solution matures." The page says the system has been "In production since April 2026". These are Awiant's statements, and the time figure is the customer's estimate as Awiant reports it. Outside Anthropic's Select tier, these facts come from Awiant's own pages.

Cay Digital. Its agents page says, "We build agent workflows and run them on Agent Mission Control, where your people manage every agent, what it costs and what it returns." [6] Agent Mission Control is Cay Digital's own product, so client agents run on a product it owns. The page says, "Agents ask before they guess: a person approves, with an SLA on every request." An SLA is a promised response time. Its homepage says it measures "the return in dollars per workflow". Outside Anthropic's Select tier, these facts come from Cay Digital's own pages.

Zemoso Labs. Its FAWM page says the method "reads your systems, SOPs and interview transcripts, maps the jobs your teams actually do, and scores each one before anyone builds an agent." [7] SOPs are the written steps staff follow. For jobs it marks as shared between a person and an agent, it says it designs "the oversight screen, exception path and handoff" before the agent is built. Outside Anthropic's Select tier, these facts come from Zemoso's own pages.

Diverger. Its homepage says, "We deploy Gemini Enterprise on your real processes: agents connected to your data, governance from day one." [15] Gemini Enterprise is Google's AI product for companies. The same homepage shows "+40% Time savings on everyday office tasks" and does not say where the figure comes from. Anthropic lists Diverger at the Select tier.

HAVIT. Its AI implementation page offers to build MCP servers connected to a client's existing APIs, so that agents can read data and take actions inside the client's systems [11]. MCP, the Model Context Protocol, is a standard way to connect an AI model to a company's tools and data, and an API is the way one program asks another for data. Anthropic lists HAVIT at the Select tier.

Bounteous. Its Anthropic partner page says it designs and deploys "AI agents, multi-agent systems, agentic workflows" [17]. A multi-agent system is one in which several agents pass work between them. Bounteous says its own Agent Management Platform includes "built-in governance and human oversight". Outside Anthropic's Preferred tier, these facts come from Bounteous' own pages.

First Line Software. Its site menu lists "Finance Operations Automate invoicing, reconciliation, and financial close" among its real estate offers [12]. Reconciliation means checking that two sets of records agree, and the financial close is the work of finishing the accounts at the end of a month or year. Outside Anthropic's Select tier, these facts come from First Line Software's own pages.

Questions for a back-office agent firm

Ask which documents and systems the agent will read and write, and who in your company approves each type of decision. Ask the firm to name the rule that decides when the agent stops and asks a person, as Awiant's "predefined criteria" and Yellow Tech's "confidence threshold" do. Ask how each published figure was measured, by whom and over how many cases. For the price of this work, read what AI agent development costs, and for the checks to run before you sign, read how to test an AI agent before you buy.