Peersol builds production-grade AI agents that handle real work — customer support, sales outreach, ops orchestration — using the latest models and frameworks, deployed and maintained by senior engineers.
"Peersol built us an AI support agent that handles 74% of tickets without human intervention. Setup was clean, the handover was thorough, and they stayed to tune it for 30 days post-launch. Outstanding work."
Describe what you want to automate — we'll assess the right AI approach live.
Peersol is a senior team of AI engineers and automation specialists with offices across the UK, UAE & Pakistan. We build production-grade AI agents for SaaS companies, ops-heavy businesses, and startups that want autonomous workflows — not toy demos.
We work across the full stack: LLM selection, prompt engineering, tool use, RAG pipelines, memory architecture, and human-in-the-loop controls. You work directly with the engineer building your agent — no account managers, no handoffs.
We pick the right tool for the job — not the one that's trending. Here's what we work with day-to-day.
We work across all major frontier models and select based on your latency, context window, cost, and accuracy requirements.
For multi-agent workflows and tool-use pipelines we use production-grade frameworks with tracing, memory, and error recovery built in.
Long-term memory, document retrieval, and semantic search via vector databases — giving your agents access to your full knowledge base.
Agents that take action across your stack — not just answer questions. CRMs, ticketing, email, calendar, and custom APIs.
Production-ready deployments with proper secrets management, logging, and cost controls — not Colab notebooks.
Full trace logging, cost tracking, LLM evaluation pipelines, and alerting so you know when an agent drifts off course.
A selection of production deployments. Most live under NDA — ask for relevant examples on the call.
An agent that handles tier-1 support tickets autonomously — pulling from a live knowledge base, updating the CRM, and escalating to humans only when confidence falls below a defined threshold. Deployed across email and in-app chat.
Inbound leads are scored against ICP criteria, personalised outreach sequences are drafted for human review, and qualified prospects are auto-booked into the sales calendar. The agent handles the manual work; the human closes the deal.
A Slack-native agent that answers operational queries by querying live databases, pulling Notion docs, and summarising project status in plain English. The ops team gets answers in seconds instead of waiting for a report to be pulled manually.
Real feedback from founders and operators who've shipped AI agents with us.
"Most AI agencies we spoke to wanted to build on OpenAI wrappers. Peersol actually understood our stack, recommended Claude for the task because of the context window, and were right. The agent has been running for 6 months without a single hallucination incident."
"What set Peersol apart was the 30-day tuning period after launch. Most agencies hand over and disappear. The team monitored the agent's outputs, caught edge cases we hadn't anticipated, and tightened the prompts without us having to ask."
"We needed a sales agent that felt natural, not robotic. Peersol spent two weeks on prompt engineering alone before touching the integration layer. The result is an agent our sales team actually trusts to draft outreach on their behalf."
Everything you'd want to know before booking a call.
We build task-specific agents (customer support, lead qualification, document processing, internal Q&A) and multi-agent workflows where several specialised agents collaborate on a complex task. We assess whether an agent is the right tool for your use case on the discovery call — sometimes a simpler n8n automation is a better fit, and we'll tell you that rather than oversell AI.
We're model-agnostic. We work with OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet / Opus), Google Gemini, and open-source models like Llama 3 for cost-sensitive or on-premise deployments. For orchestration we use LangChain, LangGraph, CrewAI, and AutoGen depending on the architecture. We'll recommend the stack based on your latency, accuracy, and budget — not what we're most comfortable with.
A focused single-agent build (e.g. a support agent or a lead scorer) typically takes 3–5 weeks from kick-off to production. Multi-agent systems or agents requiring custom RAG pipelines with large document corpora take 6–10 weeks. We give you a fixed timeline and weekly demo cadence so you're never in the dark about progress.
Projects start from £3,500 for a focused single-agent build. Most client engagements fall in the £6,000–£18,000 range depending on complexity, number of tool integrations, RAG pipeline requirements, and the level of evaluation and monitoring infrastructure included. We quote fixed-price so you know the number before we start, with no hidden costs for model API usage during development.
We design for data minimisation by default — agents only access the data they need for the specific task. We can deploy entirely within your infrastructure (AWS, GCP, Azure) with no data leaving your environment, or use enterprise API tiers of OpenAI and Anthropic that exclude training data usage. We sign NDAs before any scoping discussion and can provide DPA documentation for GDPR compliance.
Yes. All projects include a 30-day post-launch period where we monitor agent outputs, catch edge cases, and tune prompts at no extra cost. After that, we offer a monthly retainer covering performance reviews, prompt updates, model upgrades, and incident response. Many clients run their agents self-managed after handover — we document everything thoroughly so your team can maintain it.
Tell us the workflow you want to automate or the process costing your team time. We'll come back with a free scoping note — or scope it live on the call.
Fill in the form and we'll come back with a free scoping note.