The short answer: hire for agents already in production, and match the firm to the job

If you run a mid-market company and need an AI agent that does real work, the shortlist is short. For a document- or quote-heavy operation with a $50,000-plus budget, Markovate and Provectus publish mid-market entry points and describe production systems, not pilots. For a first agent under $50,000, LeewayHertz lists a $10,000 minimum on Clutch and 10Clouds reports 120-plus AI deployments. For a customer-facing agent in retail or beauty, Master of Code Global and BotsCrew have the deepest conversational portfolios here. The reason to be picky: Gartner predicts more than 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls (Gartner, press release, June 2025).

How this list was built: the criteria, and what was deliberately ignored

Every company here was checked against its own website and, where one exists, its Clutch listing on September 20, 2026. Nothing comes from memory, sales calls or third-party rankings. When a company does not publish a figure, the table says "not published" rather than guessing.

Four filters decided who made the list. First, the company sells agent development as a service; platform vendors that license software rather than build it (Botpress, for example) were left out. Second, the firm describes agentic or LLM-based systems in production, not only research. Third, there is a plausible mid-market fit: a published minimum project size below $100,000, or named case work with companies outside the Fortune 100. Fourth, the list mixes regions, because a US buyer will realistically compare a domestic firm with a Polish or Indian one on price.

The order is by fit to a mid-market agent project, not by headcount or revenue. A 6,000-person engineering firm can do this work, but it is rarely the right call for a $60,000 first project, so size counted against a company as often as for it. Awards, directory badges and hourly rates alone were ignored.

Two numbers explain the bias toward production experience. Gartner counted only about 130 of the thousands of vendors calling themselves "agentic AI" as genuinely agentic (Gartner, June 2025). And McKinsey found 40% of large organizations scaling AI agents, up from 27% a year earlier, while smaller organizations stayed flat at 22% (McKinsey / QuantumBlack, State of AI 2026, 1,719 respondents).

Comparison table: ten firms by focus, published budget, timeline and region

Budget and timeline columns show only figures the company publishes on its own site or Clutch listing. "Not published" is not a sign the firm is expensive or slow.

The ten companies, one at a time: what they do, what they have built, and when to look elsewhere

Each entry ends with a sentence on when not to pick the firm, the part most vendor lists leave out and the one that saves you a wasted discovery call.

1. Markovate: agentic AI with a manufacturing and insurance bent, San Francisco

Markovate builds custom generative and agentic AI systems for manufacturing, healthcare, insurance, construction and real estate, alongside chatbots, computer vision and MLOps. Founded in 2015, it describes a 50-plus core team. Its most distinctive asset is CADIAM, an AI Blueprint Classifier that reads CAD drawings and automates quotations.

A representative project is the AI Blueprint Classifier built for MPP Innovation, endorsed by the client's COO on Markovate's homepage; the firm also lists CodmanAI, a medical coding solution. Clutch shows a $50,000 minimum project size and a $50–99 hourly rate.

Pick Markovate when your agent has to read drawings, documents or claims and turn them into a quote or a decision. Do not pick them if your budget sits below the published $50,000 minimum, or if you need to know where the team sits: the site mentions four locations but names none.

2. LeewayHertz: the lowest published entry point on the list, with a platform behind it

LeewayHertz builds custom AI platforms, generative AI solutions and AI agents, and runs its own ZBrain platform. Clutch lists the firm as founded in 2007 with 50–249 staff in Gurugram, India; the site states that the company was acquired by The Hackett Group (NASDAQ: HCKT). The published stack spans GPT-5.2, Claude, Gemini, Llama 4, Mistral, crewAI and AutoGen Studio on AWS.

Representative work includes Scrut, an LLM-powered compliance application, plus app development for O'Reilly Auto Parts and application enhancement for Siemens. Clutch shows a $10,000 minimum project size and a $50–99 hourly rate, the lowest floor on this list.

Pick LeewayHertz for a first, tightly scoped agent when you want a low entry price and are comfortable building on ZBrain. Do not pick them if you need a team in a US time zone by default; no office list is published, and since India's most common Clutch band is $25–49 an hour (Clutch pricing guide, September 2026), ask what the higher listed rate buys.

3. 10Clouds: Claude-based agents and workflow orchestration from Warsaw

10Clouds is an AI solutions and software development company with 17 years of operation, offering custom AI agents and assistants, credit automation, workflow orchestration and agentic commerce. It is an Anthropic (Claude) partner, runs a proprietary tool called AIConsole, and reports 120-plus AI deployments. Team size is not published.

Named clients on the about page include PZU, a Polish insurer, Trust Stamp in identity verification, and Displate, a marketplace, a spread that maps onto the claims, verification and order workflows most mid-market buyers bring.

Pick 10Clouds when your workflow has clear steps an agent can orchestrate and you are open to a Claude-first stack at Polish rates, most commonly $50–99 an hour (Clutch, 2026). Do not pick them if you require published pricing before a first call, or if your compliance team insists on a vendor with a US legal entity, which the site does not show.

4. Provectus: a systems integrator for agents that must survive contact with production

Provectus is an AI systems integrator that delivers production AI infrastructure and implementations, focused on healthcare, life sciences and financial services. Founded in 2010 and based in Palo Alto, it describes 400-plus AI builders and 50-plus ML researchers. It reports "100+ customers in production" and works across Anthropic, OpenAI and Cohere models on AWS, Google Cloud, Azure and Databricks.

The about page names no customers, so there is no representative case to cite. What the site does publish is a contracting model: milestone-based agreements, with Clutch showing a $25,000 minimum project size and a $50–99 hourly rate.

Pick Provectus when the hard part is not the prompt but the plumbing: data pipelines, evaluation, monitoring and the cloud infrastructure an agent needs to keep running. Do not pick them if you want a boutique that puts the founders on your account, or if you need a named case study in your industry before signing.

5. A Culver City studio where agents live inside 3D product experiences

When the agent has to sit inside a visual product experience, a configurator, a 3D catalog or a WebAR try-before-you-buy flow, the team at Todor3D builds both halves in-house. Founded in 2020, the studio has 40-plus engineers across three continents, 300-plus delivered projects, and 25 reviews with a 5.0 rating on Clutch. Its practices are 3D and Immersive, Custom Software Engineering and AI Solutions, on WebGL, Three.js, React Three Fiber and WebAR/WebXR, with a presence in Culver City, California.

The studio publishes its brackets: $10,000–50,000, $50,000–100,000 and $100,000–250,000-plus, with timelines of 4–10 weeks, 3–6 months and 4–12 months. The homepage lists a closet configurator and a jewelry configurator, both in the $10,000–50,000 bracket.

Pick the studio when a sales agent needs to drive a 3D product, answer questions against a configurator's rules, or hand a rep a quote with the configured model attached. Do not pick them if your agent is pure back-office with no visual surface; the track record is shorter than the 2004 and 2007 vintages here, and AI is one of three practices, not the whole business.

6. Master of Code Global: conversational AI for retail and beauty, Redwood City

Master of Code Global calls itself a consulting-led AI engineering partner, covering conversational AI, generative AI and custom software through an assess, build and deploy model. Founded in 2004 with 150-plus employees, it is headquartered in Redwood City, California, with offices in Winnipeg, Poland and Kyiv. The stack spans Claude, OpenAI, Rasa, Parloa, Salesforce, Amazon Connect, Google Cloud and Apple Messages for Business, described as 15-plus conversational AI platforms.

The client list is the strongest retail roster here: Burberry, Estée Lauder, La Mer and Tom Ford Beauty, plus e-commerce names such as BloomsyBox. Pricing and timelines are not published.

Pick Master of Code when the agent's job is to talk to customers across chat, messaging and voice, especially in retail, beauty or telecom, and you want a vendor already integrated with your contact-center platform. Do not pick them for an agent that never faces a customer, such as document processing or internal approvals.

7. Neurons Lab: agentic systems for financial workflows, London and Singapore

Neurons Lab develops agentic AI systems from discovery through to production and offers AI capability training alongside the build. Founded in 2019 and headquartered in London with a Singapore office, it describes 50-plus AI engineers, architects and analysts across Europe and reports 100-plus implementations. Its focus is financial-services workflows; listed partners include AWS, Google Cloud, Azure, Visa and Projective Group.

The about page names no clients, so there is no representative project to describe. What stands out is the shape of the offer: a discovery-to-production path plus training, which matters for a finance team that intends to own the agent after handover.

Pick Neurons Lab when your agent touches credit, onboarding, reconciliation or another regulated finance workflow and you operate in Europe or Asia. Do not pick them if you are a US company with no European operations, since no US office is listed, or if your board needs a named case study before approving the budget.

8. BotsCrew: bespoke conversational agents with a US-heavy footprint

BotsCrew builds custom AI agents, generative AI and conversational AI for enterprises and startups in retail, healthcare, HR and marketing. Founded in 2016 and headquartered in San Francisco, it lists four more US offices and a delivery center in Lviv, Ukraine. The published stack includes GPT-5, Llama 3, RAG and NLP.

Representative projects on the homepage include a launch-campaign chatbot for Honda's HR-V, work for Samsung NEXT, and a conversational experience for the FIBA Basketball World Cup. Team size and pricing are not published.

Pick BotsCrew when you want a conversational agent for a campaign, a product launch or an HR or marketing process and you value US account management with a lower-cost engineering base. Do not pick them for heavy data-platform or MLOps work, or if procurement needs a published team size and rate card.

9. Softweb Solutions: enterprise-grade agentic AI with industrial roots, Dallas

Softweb Solutions, an Avnet company, covers AI, data engineering and analytics for enterprises, including agentic AI, generative AI, computer vision and edge AI. Based in Plano/Dallas, Texas, with offices in Chicago and Ahmedabad, it describes 120-plus AI and data specialists and cites case work with semiconductor and industrial manufacturers. The founding year is not published.

Named clients on the homepage include Hennig Inc., Bosch and the University of Pennsylvania, a mix that suggests the firm can scale a project down as well as up.

Pick Softweb when your agent needs plant or edge data, such as machine telemetry, inspection images or ERP records from a factory floor, and you want a US contract with an Avnet balance sheet behind it. Do not pick them if you want a small, founder-led shop or a fixed-price first sprint; nothing is published on price or timeline, and the positioning is enterprise.

10. InData Labs: the data-science layer under the agent, Cyprus with US and Lithuanian offices

InData Labs is a data science and AI solutions provider working in machine learning, generative AI, big data architecture and custom software, with experience in adtech, e-commerce, logistics, fintech, digital health and IoT. Founded in 2014, the company has 80-plus employees, a Cyprus headquarters and offices in the USA and Lithuania, and works on AWS and Databricks.

Representative projects include an anti-fraud solution for Wargaming, freight prediction software for AsstrA, and social media data analytics for Captiv8. Pricing is not published.

Pick InData Labs when the agent is only as good as the model underneath it, such as a pricing, fraud or forecasting agent that needs feature engineering before any LLM gets involved. Do not pick them if what you need is a conversational front end on top of an existing system; agents are not the firm's headline, and it does not advertise the conversational tooling that Master of Code or BotsCrew lead with.

How to choose for your own project: six questions that separate builders from demo shops

The pattern behind the 40% cancellation forecast is consistent: costs escalate, value stays unclear, and risk controls arrive late (Gartner, June 2025). A year earlier Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025 (Gartner, July 2024). These questions surface those failures before you sign.

1. Which of your agents are in production today, and what did each one replace? Ask for the process the agent took over, the volume it handles and who supervises it. A firm that answers with a demo video rather than a process name is selling a pilot. Provectus's "100+ customers in production" and 10Clouds's "120+ AI deployments" are statements to probe, not accept.

2. What will this cost to run per month, separate from the build? Output tokens cost roughly 4–6 times input tokens across OpenAI, Anthropic and Google, and cached input is about 10 times cheaper than fresh input (derived from the three vendors' pricing pages, checked September 2026). For scale, Claude Sonnet 5 lists at $2 per million input tokens and $10 per million output (Anthropic pricing page, 2026). Ask the vendor to model your monthly volume at list price. Google's Gemini 3.8 Flash price of $0.75 input and $3.75 output is scheduled to double on January 1, 2027 (Google AI for Developers pricing page), so a quote built on promotional rates has a cliff in it.

3. Who owns the prompts, evaluation sets and pipelines when we leave? If the vendor keeps the eval set, you cannot safely change models or vendors later. Platform-led firms such as LeewayHertz with ZBrain, or 10Clouds with AIConsole, deserve a direct question about what runs without their tooling.

4. What is the smallest scope you would sign, and what does it cost? GoodFirms's survey of 100-plus software companies puts a custom AI-powered MVP at $50,000–125,000 and a medium project at $125,000–250,000 (GoodFirms, survey September–October 2025). Clutch reports that most reviewed software projects fall in the $10,000–49,000 band (Clutch pricing guide, September 2026). A vendor whose smallest engagement sits far above these numbers is not a mid-market vendor.

5. How do you handle risk controls: human-in-the-loop, logging and rollback? Ask where a human approves an action, how every decision is logged, and how you switch the agent off without breaking the process it sits in. If the answer is "we can add that later," the quoted price is not the real price.

6. What happens to the budget after go-live? Agencies surveyed by GoodFirms budget maintenance at 15–25% of build cost per year (GoodFirms app cost survey, 2026), before model bills. About 20% of organizations already say AI-related operating costs constrain their AI use (McKinsey State of AI 2026). A fixed support tier and a token-cost dashboard are easier to budget for than hourly billing for every prompt tweak.

FAQ: the questions buyers ask before hiring an AI agent development company

How much does it cost to build an AI agent for a mid-size business?

Published entry points on this list start at $10,000 (LeewayHertz), $25,000 (Provectus) and $50,000 (Markovate), all per Clutch, and one studio publishes brackets from $10,000–50,000 up to $100,000–250,000-plus. Surveys land in the same range: GoodFirms puts a custom AI-powered MVP at $50,000–125,000 and a medium project at $125,000–250,000 (GoodFirms, 2026 survey). Add monthly model and hosting costs and 15–25% of build cost per year for maintenance (GoodFirms, 2026).

How long does it take to develop an AI agent?

Only one company on this list publishes timelines: 4–10 weeks for the smallest bracket, 3–6 months for mid-sized work and 4–12 months for the largest. Any number a vendor gives before discovery is an estimate. Integration work, connecting the agent to your CRM, ERP or ticketing system and building the evaluation set, takes longer than the agent logic itself, so ask for the timeline of the integration, not the prototype.

What is the difference between an AI agent and a chatbot?

A chatbot answers; an agent acts. An agent takes a goal, calls tools such as your order system or document store, and completes multi-step work, often with a human approving certain steps. Gartner expects 15% of day-to-day work decisions to be made autonomously by agentic AI by 2028, up from 0% in 2024 (Gartner, June 2025). Master of Code and BotsCrew lead with conversation; Provectus and InData Labs lead with the systems underneath.

Should we build an AI agent in-house or hire a development company?

Hire out the first agent if no team of yours has shipped an LLM system to production, and insist on owning the code, prompts and evaluation set so you can bring the second one in-house. McKinsey found smaller organizations flat at 22% scaling agents while large ones rose to 40% (McKinsey State of AI 2026); the gap is mostly capability. A vendor that offers training alongside delivery, as Neurons Lab does, shortens that path.

Why do so many AI agent projects get cancelled?

Gartner names three causes: escalating costs, unclear business value and inadequate risk controls (Gartner, June 2025). In practice that means a pilot never tied to a process metric, a token bill nobody modeled, and no plan for what a human does when the agent is wrong. The six questions above target those three failures.

Do mid-market buyers actually want AI in the sales process?

Yes, on both sides of the table. Gartner's 2025 survey of 646 B2B buyers found 67% prefer a rep-free buying experience and 45% used AI during a recent purchase (Gartner, press release, March 2026). Salesforce reports that nine in ten sales teams use AI agents or expect to within two years, though Salesforce sells agents itself (Salesforce, State of Sales, February 2026). For a mid-market company, a sales or support agent is now a buyer expectation, not an experiment.