Customer-service agents that resolve tickets. Automations that clear back-office queues. Copilots that answer from your own documents. Built on leading models, connected to your tools and data, and tested before they ever talk to a customer.
Custom AI agents that do real work: customer-service agents that resolve tickets, sales assistants that qualify leads, internal copilots that answer from your own documents. Built on leading models, connected to your tools and data, and tested before they ever talk to a customer.
We map the repetitive work inside your operations and automate it: document processing, invoice handling, lead routing, reporting, email triage. You keep the judgment calls; the busywork disappears.
The highest-return deployments we see, again and again.
Resolve common tickets end to end, escalate the rest to a human with full context.
Qualify inbound leads, answer product questions, and book meetings into your calendar.
Answer staff questions from your own documents, policies, and knowledge base (RAG).
Extract, classify, and file information from contracts, forms, and PDFs automatically.
Capture, match, and route invoices for approval — with humans on the judgment calls.
Score and route incoming leads to the right person while they're still warm.
Turn scattered data into scheduled, plain-language reports nobody has to assemble.
Sort, summarize, and draft responses for shared inboxes that eat entire mornings.
Anyone can wire up a chatbot. We ship agents with evaluation tests, output constraints, and monitoring — so they still work in month six, not just in the sales call.
Agents and automations connect to your CRM, ERP, helpdesk, and data. No parallel tools to maintain, no swivel-chair work between systems.
Automation handles the busywork; your team keeps the judgment calls. Approval checkpoints go exactly where the risk is.
Every deployment ships with tracking: tickets resolved, hours saved, documents processed. If we can't measure it, we won't sell it to you.
You tell us the goal; we tell you what it would take, what it would cost, and whether AI is even the right answer.
We map the work worth automating and write a fixed-scope proposal: deliverables, timeline, price.
Agent or automation built against your tools and data, with evaluation tests before anything touches a customer.
Production monitoring, plain-language reports, and iteration as your processes evolve.
PixelGrid builds custom AI agents that do real operational work rather than demo-grade chat. Customer-service agents connect to your helpdesk and resolve common tickets end to end — order status, password resets, returns eligibility — and escalate the rest to a human with the full conversation attached. Sales assistants qualify inbound leads, answer product questions from your actual catalogue and pricing rules, and book meetings straight into your calendar. Internal copilots answer staff questions from your own documents, policies, and knowledge base, so answers reflect how your company actually works. Every agent is connected to your existing tools through their APIs, restricted to the data it needs, and run through evaluation tests before it ever talks to a customer. We scope each agent around one job it can do well, then expand from there.
We build on leading commercial and open-source models and choose per use case, weighing four factors: accuracy on your task, response latency, data-privacy requirements, and running cost. A customer-facing support agent usually justifies a stronger model; a high-volume internal classification step often runs better on a smaller, cheaper one. For European clients with data-residency requirements we can select providers with EU processing, or deploy open-source models where data cannot leave your infrastructure at all. The architecture is model-agnostic wherever possible: prompts, retrieval, and integrations are structured so the underlying model can be swapped as the landscape changes — which it does every few months. That means you are not locked into a single vendor's pricing or capabilities, and upgrades are a configuration change rather than a rebuild.
Three layers, applied in order. First, retrieval (RAG): the agent answers from your actual documents, product data, and policies rather than from the model's memory, and its answers reference the source passages it used. Second, guardrails and evaluation: before launch we build a test suite of real questions with known correct answers — including trick questions the agent should refuse — and the agent has output constraints, such as never quoting a price that isn't in your price list. Third, human-in-the-loop checkpoints: actions with real consequences, like issuing a refund or sending a contract, route to a person for approval, and when the agent's confidence is low it hands the conversation to a human rather than guessing. In production we monitor answer quality continuously, because model quality can drift quietly over time.
The reliable wins are repetitive, rule-adjacent work that currently eats staff hours. Document processing: extracting and filing information from contracts, forms, and PDFs. Invoice handling: capturing invoice data, matching it against purchase orders, and routing exceptions for human approval. Lead routing: scoring and assigning incoming enquiries to the right person while they are still warm. Reporting: assembling scheduled, plain-language reports from data scattered across systems. Email triage: sorting, summarising, and drafting responses for shared inboxes that consume entire mornings. We start every engagement with a workflow audit to find where automation actually pays back, measured in hours saved per week — not where it merely demos well. The dividing line: busywork gets automated, judgment calls stay with your team, and every automation ships with an approval checkpoint exactly where the risk sits.
Yes — integration is the point, not an add-on. Agents and automations connect to the tools you already run through their APIs: CRMs such as HubSpot or Salesforce, helpdesks such as Zendesk or Intercom, accounting and ERP systems, shared inboxes, calendars, and internal databases. Where a system has no usable API, we work through middleware, webhooks, or structured exports rather than asking you to replatform. Each integration is scoped to the minimum access the agent needs — a support agent that reads order status does not get write access to your accounts. During the workflow audit we map exactly which systems are involved in each process, so the proposal you receive lists the integrations by name before any build starts. You keep your existing tools; the AI plugs into them.
It depends on scope, which is why every project starts with a free 30-minute consultation rather than a rate card. As a general shape: automating a single well-defined workflow, like invoice capture or email triage, is a smaller engagement than a customer-facing agent, which needs integration, evaluation testing, and monitoring before launch. After the consultation and a workflow audit you receive a fixed-scope written proposal listing deliverables, timeline, and price — no open-ended hourly billing, no surprises at handover. We deliberately favour starting small: ship one automation, measure the hours it saves, then expand to the next process with evidence in hand. And if we think AI is the wrong answer for your problem, we will say so in the first call — including when you don't need us yet.
Book a free 30-minute consultation. You'll leave with a concrete recommendation — whether or not you hire us.