Support agent
Reads incoming tickets, pulls answers from your docs and past resolutions, replies in your voice
Now booking Q3 engagements
Custom AI agents,
built for your operators
Reads your tickets, qualifies your leads, or clears a backlog of documents. Trained on your data. Graded against a metric you set before it reaches production
Five agents, five
distinct jobs
Multi-purpose assistants tend to spread themselves thin across too many responsibilities, so each agent here is built around a scoped function instead, trained on your data and measured against a metric you define before it ever reaches production
Sales agent
Qualifies inbound before your team. Asks what a senior rep would ask. Books the call when it is worth it
Internal Ops agent
Works inside your tools: looks up records, triggers the next step, keeps systems in sync
Document agent
Opens each file, extracts what matters, drafts a response, waits for approval
Workflow agent
Runs a multi-step process from trigger to final approval, with retries and a log of every step
Something else in mind?
If your workflow falls outside the five above, we'll scope and build the agent that does
Recent production
builds
In modern production systems running on customer infrastructure, every metric is measured relative to the baseline that operators were using before the launch
Support
B2B SAAS
Support agent handling 62% of tickets autonomously
−41%avg. handle time
Sales
B2B Marketplace
Inbound qualifier routing 1.2k leads weekly
+28%SQL conversion
Ops
Logistics SaaS
Internal ops copilot over 14 data sources
5h/wksaved per analyst
Docs
Legal Tech
Contract review agent across 9 deal types
98.4% clause accuracy
How an agent gets built
The six- to eight-week process transforms a complex operational problem into a production system that your team can master and use from the ground up
Discovery
The engagement opens with two days on-site or on-call, spent mapping the workflow, the data behind it, and the people who are doing the work today
Architecture
This phase produces the agent contract, its tool surfaces, the evaluation harness, and a rollout plan, all reviewed together with your engineering lead
Build
The build stage runs as iterative cycles against your production data, with weekly checkpoints alongside stakeholders so every deliverable stays visible along the way
Evaluation and deployment close the pipeline. The full five-phase breakdown lives on the process page
See the full pipelineA workflow that
should run
without automatically
Send the operation you want automated. Written assessment within 48 hours: agent shape, scope, timeline.
The stack behind
every build
The stack stays model-agnostic and framework-pragmatic, picked for what the job requires and engineered to outlast whatever the next vendor release changes
Models
- OpenAI
- Anthropic
- Gemini
- Open-source LLMs
Frameworks
- LangGraph
- OpenAI Agents SDK
- Vercel AI SDK
- Pydantic AI
Infra
- AWS
- GCP
- Cloudflare Workers
- Modal
Data
- Postgres
- Pinecone
- Snowflake
- S3 / R2
SOC 2 aligned
Audited build practices, secrets management, and data handling
Your infra, your keys
We deploy into your cloud or ours. Either way, you own the system
Versioned and testable
Every prompt, tool, and policy lives in Git with eval coverage
What a generic
agent skips
Every ready-made agent is designed for thousands of companies. Your company isn't one of them. That's what changes when an agent is tailored to your workflow
A generic agent is designed for the median customer, so the ten hardest cases in your operation — the ones your team actually spends its week on — arrive as someone else’s roadmap ticket
Everything downstream follows from that. Your data has to move into the vendor’s schema before the agent can read it, and the connectors stay theirs. Behaviour is tuned through prompts in a settings panel, so reproducibility ends the moment the model behind it updates. Throughput is capped by a plan, and the more of your operation runs through the agent, the more constrained you are by it. When the vendor pivots, your workflow pivots with them
Built the other way around. It is specified against your hardest cases, reads your systems where they already live, and ships versioned in Git with an evaluation suite, so a regression in week thirty is caught before it reaches production. It runs on your infrastructure and your model, scaling the way your operation does — and at hand-off the repo, the prompts, the tools and the evals are yours
Priced by what the
agent has to do
Each project is completed within a fixed scope of work, depending on the complexity of the agent being executed, and once the project is completed, you retain ownership of the production system
Launch
BEGINS$28k
A dedicated agent, built around a specific workflow and a specific system of record
- 1 agent, 1 operational job
- Up to 3 tool integrations
- Data pulled from your existing source
- Eval harness + 14-day on-call
Workflow
BEGINS$65k
Multi-step workflows across several systems with human review
- Multi-step orchestration
- Up to 8 tool integrations
- RAG over your knowledge base
- Human-in-the-loop approval
- Eval suite + 30-day on-call
Fleet
custom
A fleet of agents, your infra, your governance
- Multi-agent system, shared memory
- Unlimited integrations
- Self-hosted in your cloud
- SOC 2 / HIPAA aligned delivery
- Quarterly retraining + dedicated lead