A production-grade AI system that connects the right model to the right context and actions, with guardrails that keep it reliable at scale.
Most “AI agents” fail because they’re built as demos: they can talk, but they can’t finish work. We build agents that do the boring, high-volume tasks reliably—inside your real tools and with your real constraints. Examples: CRM hygiene (dedupe, field normalization, lifecycle stages), lead enrichment + qualification, inbound routing, doc intake + extraction, support triage, and content re-alignment to buyer pain points. Key point: the agent isn’t a new app your team has to learn. It’s a capability embedded into your existing stack.
A modern company doesn’t need more prompts. It needs pipelines: data in → logic → model calls → validation → actions → logs. We build end-to-end workflows that connect your systems (CRM, CMS, data warehouse, support desk, internal docs) into repeatable automation that runs unattended. We use platforms like n8n, Google’s agent frameworks, and custom orchestration where needed. Guardrails are part of the design: permissioning, rate limits, validation checks, fallbacks, and human-in-the-loop steps when the cost of being wrong is high.
Generic model answers are cheap. Useful answers require your company’s context. We build retrieval systems grounded in internal reality: SOPs, product catalogs, policies, enablement docs, implementation notes, support history, and past decisions. The model avoids “guess” as much—it cites, references, and stays consistent with your actual source of truth. Outcome: faster onboarding, fewer repeated questions, and less tribal knowledge trapped in Slack and people’s heads.
If you can’t measure it, it will drift. Every deployment includes evaluation. We set up scoring metrics (accuracy, completeness, hallucination rate, action success rate), test sets based on real cases, and feedback loops from users and outcomes. Then we run controlled variations and track scores so the system improves over time—like a product, not a one-off build. This is how agents become dependable: not by better prompts, but by tight loops and visible performance.
Deploying AI comes down to context. Situational, historical, and institutional data give agents the full picture before they act. Change signals and real-time feeds add the awareness generic chatbots never have.
Prompt engineering alone scales linearly. We build agents with tool access, persistent memory, and retrieval systems — then run automatic optimization loops with controlled variations and tracked scores to unlock use cases that static prompts cannot reach.
When agents take actions, accountability has to be built in. Every workflow includes an auditable trail of what triggered the action, what systems were touched, what changed downstream, and how to roll it back.
We design exception handling and human checkpoints into every workflow. AI handles the volume; your team makes the judgment calls. Trust comes from consistent outcomes over time.
FAQ about our AI agents and workflows
An AI agent is software that understands context, makes decisions, and takes actions across your tools to complete end-to-end tasks, not just answer questions.
A chatbot mainly returns text. An agent can read and write data, trigger workflows, call APIs, and log what it did for audit and optimization.
For B2B SaaS: lead enrichment and routing, SDR/AE follow-up, L1 support triage, onboarding, renewal and churn-risk outreach, and recurring reporting.
For agencies: research, brief creation, campaign QA, performance summaries, competitor monitoring, and RevOps/analytics tasks like pipeline health checks.
Agents increase qualified pipeline and deal velocity by prioritizing high-intent accounts, speeding up follow-up, and personalizing outreach.
They improve margins by removing repetitive work, increasing billable utilization for agencies, and reducing churn and support workload for SaaS teams.
We connect to CRMs such as Salesforce, HubSpot or Pipedrive; support tools like Zendesk or Intercom; marketing platforms; project tools such as Asana, ClickUp or Notion; data warehouses; and internal knowledge bases.
Agents orchestrate actions through APIs, webhooks, and workflow engines so data stays where your teams already work.
We encode ICP definitions, qualification rules, sales stages, and delivery processes directly into the agent’s logic, prompts, and retrieval layer.
The agent is grounded in your playbooks, SOPs, and past work, with approvals added where nuanced decisions matter.
We use RAG so the agent draws from your internal content—docs, SOPs, product specs, policies, and historic tickets—instead of guessing.
Prompts are constrained, outputs are validated, and evaluation sets keep responses accurate and consistent over time.
Read-only access to the systems involved (CRM, ticketing, project tools, knowledge base, internal docs), plus representative historical examples of leads, tickets, campaigns, or workflows.
Those examples become the basis for test sets, guardrails, and evaluation so the agent behaves correctly in real cases.
Prompt-only automations are brittle and single-step. They don’t manage tools, memory, retrieval, or feedback loops.
Our approach combines models with tools, RAG, state, guardrails, and evaluation so multi-step workflows stay reliable over time.
No. We handle AI architecture, model selection, orchestration, and evaluation. Your team provides domain knowledge and access to systems.
If you have data/engineering teams, we align to your infrastructure, SSO, and governance standards.
Least-privilege access, environment isolation where needed (including per-client setups for agencies), and alignment with your controls.
Governance and audit trails show how AI is used and what data is accessible to which workflows.
We can route to frontier APIs, open-source models, or your existing vendor depending on performance, cost, and data requirements.
If you already have an internal platform, we integrate with it while keeping the same workflow and evaluation layers around it.
Metrics are agreed upfront: qualified opportunities created, time saved per task, reduced backlog, faster SLAs, higher utilization, lower churn drivers.
Agents log actions and outcomes so gains can be attributed to specific workflows and iterated based on impact.
Most teams see value from a first workflow in roughly 4–8 weeks once access is granted and the scope is defined.
Larger cross-department programs take longer; pilots are selected to be high-impact and low-risk to build momentum.
We map workflows, time sinks, and the data landscape, then score candidates by business impact and implementation difficulty.
One to three pilots are selected—often sales ops, CS, or reporting—so quick wins land before scaling.
No. Agents remove mechanical, repetitive work. Strategy, relationship-building, and complex negotiations stay with people.
Your team stays accountable for outcomes; agents provide an always-on execution and analysis layer.
Yes. Internal automations often become client offers: AI-augmented demand gen, automated reporting, RevOps co-pilots.
We help design workflows, documentation, and SLAs so these services can be priced and delivered under your brand.
Workflows include validation, safe defaults, approvals for higher-risk actions, and rollback paths.
As your process evolves, prompts, logic, and evaluation sets get updated so the system stays aligned with how you operate.
Whether you’re exploring your first AI use case or need help turning a proof of concept into a production system, we’d like to hear about it. Our approach is grounded in years of building production software — not chasing the AI hype cycle.