AI automation services that put agents to work.

AI agents are software components that read a situation, weigh it against context and policy, decide what to do and then act, whereas traditional automation executes fixed steps in a fixed order and stops when an input arrives in an unexpected shape.

Traditional automation - robotic process automation, workflow tools, scripted integrations - executes fixed steps in a fixed order. It is fast, cheap and reliable right up to the moment something arrives in an unexpected shape, and then it stops.

An AI agent works differently. It reads the situation, weighs it against context and policy, decides what to do next, and can take a different path than the one it took yesterday. Put concretely: a scripted rule looks for an order number in a known position in an email and fails when the customer writes a paragraph instead. An agent reads the paragraph, works out what the customer is actually asking for, checks their history and the current inventory and pricing rules, and drafts a response a person would recognise as sensible.

That flexibility is the whole value, and it is also the whole risk. This is why most of the engineering effort in agentic AI goes into guardrails, escalation paths and measurement rather than into the model itself. Most businesses end up running both: deterministic automation where the process is genuinely fixed, and AI agents where judgement is required.

AI agent development at PixelForce is priced inside the standard engagement envelope: Phase 1 Scoping and Design typically $35,000 to $65,000, then Phase 2 Development, QA and Release typically $100,000 to $350,000.

PixelForce prices AI automation the same way it prices every build, because the phase structure is what keeps the number honest.

Phase 1 - Scoping & Design is typically $35,000 to $65,000 and is mandatory before any development quote is issued. No Blueprint, no Build. It produces the Business Requirements Document, the Product Requirements Document, the UX/UI design and a fixed-cost Statement of Work. It is a standalone commitment on purpose, so you can stop after it if the evidence says the automation is not worth building.

Phase 2 - Development, QA and Release is typically $100,000 to $350,000, driven by how many systems the agent touches, how much regulated or financial logic sits underneath it, and how many agents have to coordinate. The $350,000 figure is a recommendation rather than a ceiling: with a larger budget we still advise capping version one near it and spending the rest on evidence-led iteration afterwards.

Phase 3 - Post Launch Support has two options. Warranty, Monitoring & Support is $4,000 per month. The Product Retainer includes everything in Option 1 and is priced per four-week cycle, from Steady at $10,000 to Momentum at $50,000, with Enterprise on application.

Every figure is an envelope shaped by scope, never a fixed quote off a rate card. Model and inference costs from providers such as OpenAI, Anthropic or AWS Bedrock are billed separately by those providers on their own published pricing and are not included in the phase figures quoted here.

How long AI agents take to build is decided by integration rather than by the AI, and shipped work sets the range: 2 weeks for Crop Shop Boutique, 4 weeks for the EzLicence Handbook, 4 months for OpBill.

Long enough to scope it properly, and less time than most people expect once it is scoped. Rather than quote a generic range, here is what we have actually shipped.

The EzLicence AI knowledge system, The Handbook, went from start to live in 4 weeks. It consolidated seven years of product evolution into a single source of truth, delivers a 50 percent efficiency gain across workflows, and automates 90 percent of documentation updates with 10 percent human oversight.

For Crop Shop Boutique we connected ShipBob, Klaviyo and Shopify in a 2-week development cycle: 100 percent of pre-shipment customer notifications automated and the manual CSV export workflow eliminated.

The OpBill AI-powered OCR claiming flow was built in 4 months and made medical billing 90 percent faster with 98 percent user satisfaction.

The variable that moves a timeline most is not the AI. It is integration. An agent talking to well-documented modern APIs moves quickly. An agent that has to reach into an undocumented legacy system, or into data that has never been cleaned, takes considerably longer - and we identify that during Phase 1 rather than discovering it mid-build.

AI agents suit business processes that are repetitive, multi-step, judgement-light but context-heavy and expensive in people's time, which in practice means internal knowledge and documentation, operations and fulfilment, and document and claim processing.

The processes worth automating share a shape: repetitive, multi-step, judgement-light but context-heavy, and expensive in people's time. In practice they cluster in a few places.

Internal knowledge and documentation, where an agent keeps a source of truth current. That is exactly what The Handbook does for EzLicence, automating 90 percent of documentation updates with 10 percent human oversight.

Operations and fulfilment, where an agent watches systems and triggers the right action. For Crop Shop Boutique the integration we built now automates 100 percent of pre-shipment customer notifications.

Document and claim processing, where extraction plus decisioning replaces typing. The OpBill AI-powered OCR claiming flow made medical billing 90 percent faster with 98 percent user satisfaction.

Beyond those, common candidates include support triage and drafting, lead qualification and enrichment, invoice and expense categorisation, compliance checking, and recurring reporting.

What matters more than the list is the filter. During Phase 1 we look at volume, the cost of an error, how stable the process actually is, and whether a person still has to check the output anyway - because if they do, the saving is much smaller than it looks on a slide.

Yes, AI agents are reliable enough for production use provided they are built to fail safely, because an agent degrades quietly rather than throwing an error, which is exactly what the guardrails are designed to catch.

Yes, provided they are built to fail safely, because they do not fail the way ordinary software fails. Buggy code throws an error and stops. An agent degrades quietly: it keeps producing plausible output that is gradually less correct, and nobody notices until something downstream breaks.

Designing for that is most of the work. We set confidence thresholds so an agent that is not sure enough escalates to a person instead of guessing, and the threshold itself is a business decision made in Phase 1 rather than a technical default. We keep humans in the loop on high-consequence decisions, with the agent preparing a recommendation and a person approving it. We constrain agents inside hard boundaries - spending limits, approval chains, policy rules - so the worst case is bounded rather than open-ended. We log every decision with its reasoning so it can be audited. And we monitor quality continuously after launch rather than deploying and walking away, which is one reason the Phase 3 retainers exist.

The EzLicence Handbook illustrates the posture: it automates 90 percent of documentation updates and deliberately keeps 10 percent human oversight. That last 10 percent is not a shortcoming, it is the design.

Yes, AI agents integrate with existing systems, and integration is usually where both the real value and the real effort sit: CRM, billing, ticketing, ERP, data warehouses and anything exposing a REST API or webhook.

Yes, and integration is usually where the real value and the real effort both sit. An agent that cannot reach your CRM, billing, ticketing, ERP or data warehouse is a demo. We design agents to work inside the technology you already run rather than asking you to replace it.

Typical integration surfaces are CRM records and opportunity updates, support and ticketing queues, finance and ERP systems for invoices and reconciliation, data warehouses for querying and reporting, email and messaging platforms, and any system exposing a REST API or webhook.

Crop Shop Boutique is the clean example. We connected ShipBob, Klaviyo and Shopify in a 2-week development cycle, and the outcome was 100 percent of pre-shipment customer notifications automated and the manual CSV export workflow eliminated. Three third-party platforms, one automated flow, no change to how the business already worked.

During Phase 1 we audit your stack and confirm what each system can actually expose, then design the workflow around that reality. PixelForce is an AWS Advanced Tier Partner with 15+ AWS-accredited engineers, and the automation runs in your own cloud account with the IP transferring to you.

An AI agent is proactive and autonomous, watching systems and acting without being prompted, whereas a chatbot is reactive and answers a question a person has already asked. Both are useful and they are not competing purchases.

A chatbot is reactive. A person starts the conversation and the bot answers. That is genuinely useful for customer-facing support and self-service, and it is a bounded problem.

An AI agent is proactive and autonomous. It watches systems, notices that something needs doing, decides what to do, and does it without anyone prompting it. A chatbot answers "where is my order". An agent notices that a shipment is late, checks the carrier, drafts the customer message, and either sends it or queues it for approval.

The distinction matters commercially because it changes what you are buying. A chatbot deflects contacts. An agent removes work from a process. Most organisations eventually want both, and they are not competing purchases - the chatbot sits at the front door and the agents sit behind it.

If your interest is specifically in conversational interfaces, retrieval over your own documents, or building directly on large language models, that is covered on our generative AI and LLM development page.

PixelForce builds safety into AI agents at four levels: hard constraints on what an agent can do, escalation of uncertain decisions to a person, auditable logging of every decision, and continuous measurement of decision quality after launch.

Safety is architectural, not a review step at the end. We build it at four levels.

Constraints. An agent operates inside explicit boundaries - spending limits, approval authority, policy rules - so there is a hard ceiling on what it can do even when it is wrong.

Escalation. Uncertain or high-consequence decisions route to a person by design rather than by exception, and where that line sits is agreed with you in Phase 1.

Auditability. Every decision is logged with the reasoning and the inputs behind it, so you can answer "why did it do that" months later and use the answer to improve it.

Measurement. We test against real scenarios and edge cases before release, then track decision quality after launch and alert on degradation.

The pattern EzLicence runs is the one we recommend to most clients: automate the 90 percent that is genuinely routine and keep deliberate human oversight on the remaining 10 percent. Governance of this kind is ongoing work, which is why it belongs in a Phase 3 retainer rather than being treated as finished at launch.

AI automation services cover the design, build and operation of software that uses artificial intelligence to carry out business processes with limited human intervention. At PixelForce that spans four things.

AI strategy consulting, where we assess where automation would actually pay and, just as often, where it would not. AI agent development, where we build the autonomous components that observe, decide and act. Business process and workflow automation, where those components are wired into the systems your business already runs. And ongoing operation, because an agent is not a deliverable you accept and file - it needs monitoring, measurement and iteration to stay accurate as your business changes.

The distinction from conventional automation is that AI automation handles inputs that vary and situations nobody anticipated when the process was written down. The distinction from an AI feature inside a product is the goal: AI automation targets your internal operating cost and capacity, whereas building intelligence into a product you sell to customers is AI-powered app development.

Yes, PixelForce provides AI strategy consulting, and it happens inside Phase 1 Scoping and Design, typically $35,000 to $65,000, where two strategic workshops rank automation candidates by volume, the cost of an error and process stability.

Yes, and for most clients it is the correct starting point rather than an upsell. AI strategy consulting at PixelForce happens inside Phase 1, Scoping & Design, typically $35,000 to $65,000.

Two strategic workshops map your processes, identify where automation has genuine leverage, and rank the candidates by volume, the cost of an error, and how stable each process actually is. We use the 1-3-1 method throughout - one problem, three options with their trade-offs written down, one recommendation across budget, timeline and scope - so you can see the reasoning rather than being handed a conclusion. You leave with a Business Requirements Document, a Product Requirements Document, the UX/UI design and a fixed-cost Statement of Work for the build.

A meaningful share of that work ends with us recommending that one or two processes be automated and the rest left alone, or that the real problem is data quality rather than intelligence. Declining to build is a valid outcome here, and it is far cheaper for you to find that out in Phase 1 than in Phase 2.

An agentic workflow is a business process in which one or more AI agents carry out the steps, decide the order, and hand off to each other or to a person as the situation requires. The contrast is with a scripted workflow, where every branch is written in advance by a developer. In an agentic workflow the destination is specified and the route is decided at run time.

A simple agentic workflow uses a single agent: read the incoming item, classify it, take the appropriate action, escalate if unsure. A multi-agent workflow splits responsibilities - one agent gathers and validates information, another decides, another executes and reports - which is useful when the steps need genuinely different context or different permissions.

The engineering discipline is in the seams: what each agent is allowed to do, what it must escalate, what gets logged, and how the whole thing is measured once it is live. Those decisions are made in Phase 1 and revisited in the Phase 3 retainer as real usage shows which assumptions were wrong.

Choose between AI agents, AI-powered apps, an AI MVP and generative AI by starting from what you are trying to change: your own operating cost, the product your customers use, an unproven idea, or work that is mostly language.

Start from what you are trying to change.

If you want to reduce the cost and effort of running your own business - internal processes, operations, documentation, claims, fulfilment - that is AI automation and AI agents, the PixelForce service that owns that territory.

If you want to put intelligence inside a product your customers use, so the product itself becomes smarter, that is AI-powered app development.

If you have an AI idea that is not yet proven and you need the cheapest credible way to find out whether it works and whether anyone wants it, that is AI MVP and rapid prototyping.

If the core of the work is language - retrieval over your own documents, a model tuned on your data, a RAG pipeline, a conversational interface - that is generative AI and LLM development.

The boundaries blur in practice and plenty of engagements draw on two or three of them. Working out which one you actually need is exactly what the first consultation is for, and it is free.

Choose an AI automation company on how it decides which processes to leave alone. Ask how candidates are ranked, and listen for volume, the cost of an error, how stable the process actually is, and whether a person still has to check the output anyway.

Then interrogate the guardrails, because that is where the engineering effort genuinely goes. Ask what hard limits an agent will operate inside, such as spending caps, approval chains and policy rules. Ask where the escalation threshold sits and who decides it. Ask what is logged with each decision, and whether you could answer why the agent did something six months later. Ask how decision quality is measured after launch, not only before it.

Check integration realism. An agent that cannot reach your CRM, billing, ticketing or data warehouse is a demonstration, so ask the company to confirm what each of your systems can actually expose before it designs the workflow rather than after.

A good answer is honest about the human share. A provider claiming full automation of a judgement-heavy process is either describing a simpler process than yours or has not measured what an error costs you.