We design and build AI agents that read from your CRM, write to your ERP, query your databases, and trigger your workflows. Orchestrated with n8n, reasoning on the model that fits the job, deployed to infrastructure you own.
We've built and inherited a number of agent systems by now. The same three problems show up in nearly every conversation — and all three are solvable with engineering, not with a different vendor demo.
Most agent products handle summarisation and email drafting well. The moment a workflow needs your pricing logic, approval chain, customer tier, or compliance rules, the agent fills the gap with confident guesses. Adoption breaks in the first weeks.
Prompt chains are the easy part. The work that absorbs roadmap time is evaluation, retries, prompt drift, token accounting, vector store maintenance, and integration plumbing — none of which moves your core product forward.
An agent that can't read your CRM, update your ERP, query your database, or trigger your workflows is a conversational shell. The integration layer — auth, idempotency, retries, audit — is where most of the engineering actually lives.
Every Voyantt agent is built on the same backbone. Substitute models, tools, or data sources — the backbone keeps the system inspectable and supportable.
Your agent decides what to do, calls the right tools, handles branching logic, and manages retries. The workflow lives in n8n so your team can inspect and modify it without touching application code.
We pick the model that fits the job, cost profile, and data-residency requirement. Prompts are versioned, outputs are validated against schemas, and you can swap models when something better lands.
Your agent remembers context across conversations and sessions — user preferences, prior decisions, document references — kept in Postgres with pgvector so it sits inside your existing data stack.
When the agent needs to search large datasets — documents, calls, profiles — we deploy Qdrant for hybrid vector + keyword retrieval. We've run this at 330M-record scale in under 200ms.
Salesforce, HubSpot, Acumatica, QuickBooks, Slack, internal REST and GraphQL APIs — whatever the workflow touches. Idempotent writes, audit logs, and least-privilege access by default.
Agents deploy to AWS, Hetzner bare-metal, or your existing infrastructure. Multi-tenant if you're shipping to customers, single-tenant for internal use. Monitoring, logging, and alerting included.
Three production agents, all running today. Each one started as a focused workflow and grew under the same engineering discipline — not a tool-shop, not a notebook experiment.
A query agent that lets non-technical users ask questions of a structured database in English. The agent parses intent, generates safe SQL against a read-only view, validates the query, runs it, and returns formatted results — with every column it touched cited in the response.
An AI search engine indexing 330M+ professional profiles, returning the top matches in 50–200ms on a single bare-metal node. The agent layer interprets the query, decides which retrieval strategies to combine, and surfaces results with confidence and source context.
An agent that ingests every customer call, transcribes it, extracts a structured summary, scores sentiment, and surfaces recurring complaint patterns. Managers ask in plain English — "show me upset customers from last week" — and get calls, quotes, and adjuster handoffs.
Nothing exotic — proven components held together with engineering discipline. We swap individual pieces when your environment demands it.
Four phases. One focused workflow at a time. Production is planned around the team that will own it — not pushed onto them at the end.
30-minute call. You describe the workflow, the systems already in play, and what 'good' looks like. We come back with a focused scope or refer you elsewhere if we're not a fit.
One agent, one workflow, one model — running against your real data. You see how it behaves before any production conversation begins.
Integrations, observability, guardrails, deployment. Weekly demos, written status, decisions captured. Code lands in your repository as we go.
Phased rollout to real users, monitored. We stay on for support, hand it to your team, or both — whichever matches your operating model.
A few lines is enough — what the agent reads, what it decides, what it should do next. We reply within one business day with a scope, a model recommendation, and an honest cost range.
You do. Code lands in your repository as we build it. Prompts and orchestration definitions hand over with the agent. We can keep operating it on your behalf or pass it cleanly to your team — your decision, not a contract clause.
For a focused first agent — one workflow, one or two integrations — we can scope to a fixed budget after the brief. Broader work is dedicated-team because requirements move as the system meets reality.
GPT-4o and Claude for most reasoning work; open-source models (Llama, Mistral) when fully self-hosted inference is required for cost or residency. We architect for swap-ability so the choice isn't locked.
Four layers: input filtering, prompt constraints, output validation against structured schemas, and least-privilege access to downstream systems. Every agent decision is logged with its inputs and reasoning; nothing writes to a system of record without a validation step.
On AWS (EU regions for European clients) or bare-metal Hetzner infrastructure in EU regions (Frankfurt by default). EU data stays in the EU when contracts require it.
A working prototype against your real data takes two to three weeks. A production-ready agent typically lands in eight to ten weeks, depending on how many integrations are in scope.
Either side. We can run it on infrastructure you own, hand the runbook to your platform team, or stay on as a support engagement — whichever matches your operating model.