Support01
Customer support agents
Resolve tier-1 tickets across email, chat and WhatsApp — escalate cleanly when they should.
Agentic AI development
Production-grade evalsHuman-in-the-loop by defaultPII-safe executionYou own the code
Evals
Built in from day one
Evals
Built in from day one
HITL
Human-in-the-loop by default
5.0
Google rating
You own
The spec and the code
What we build
Sales02
Outbound & SDR agents
Research accounts, draft personalised outreach and book meetings — under your sales playbook.
Research03
Research assistants
Brief generation, market scans, competitor monitoring — with cited sources you can audit.
Ops04
Internal ops agents
Triage tickets, classify documents, update CRM records — quietly running on your back-office work.
Engineering05
Code & PR agents
Open scoped pull requests, run on-call playbooks, monitor flaky tests — alongside your engineers.
Knowledge06
Internal knowledge agents
Surface the right doc, ticket or decision — across Notion, Linear, Drive and your data warehouse.
Anatomy
01
Planner
An LLM reasoning loop that decomposes the goal into steps — and can backtrack when a step fails.
- Goal decomposition
- Reflection on failures
- Bounded autonomy
02
Tools
Typed tool calls into your APIs, SaaS apps and internal services — sandboxed, rate-limited, audited.
- Typed JSON schemas
- Rate-limit + retries
- Per-tool audit trail
03
Memory
Short-term context plus long-term vector store, scoped per tenant and per user with redaction policies.
- Per-tenant isolation
- Vector + keyword recall
- Redaction & retention
04
Evals
Offline golden sets and live production evals, so changes ship behind measurable gates — not vibes.
- Golden eval sets
- Live regression dashboards
- Cost / latency budgets
What you get
8 items, none of them extra
Custom agent built against a written specification you own
Typed tool schemas + auditable run logs for every step
Golden eval set + live production regression dashboards
Human-in-the-loop UI for any high-stakes action
Cost, latency and token-budget dashboards in Looker Studio
PII redaction + per-tenant memory isolation by default
Slack / Linear / email notification routing
30-day post-launch tuning window — prompt and eval iteration
How it runs
- 01Week 1
Workflow discovery
We sit with the team, watch the work, and pick the loop where an agent will actually compound.
Workflow map · Use-case scoring
- 02Week 2
Tool & data design
Tool surfaces, schemas and the memory model — what the agent can touch and what it absolutely cannot.
Tool schemas · Memory plan
- 03Week 2
Eval suite first
Before the agent runs in anger, we build the golden set. Changes ship behind measurable gates from day one.
Golden set · Eval harness
- 04Weeks 3–6
Agent build
Planner + tools + memory wired with bounded autonomy. Human-in-the-loop where stakes are high.
Releasable agent · HITL UI
- 05Week 7
Shadow mode
The agent runs alongside humans on real workload. We measure agreement, cost and latency before flipping the switch.
Shadow-mode report
- 06Ongoing
Production & retro
Staged rollout behind flags. Live evals, cost dashboards and a quarterly retro on what the agent should learn next.
Live dashboards · Retro
The stack
Anthropic ClaudeReasoning
OpenAIReasoning
LangChainOrchestration
Vercel AI SDKRuntime
PythonBackend
SupabaseVectors + state
SlackSurface
LinearWorkflow
FAQ
- 01How is an AI agent different from a chatbot?
- A chatbot answers. An agent does. It plans a goal into steps, calls real tools in your stack (CRM, ticketing, internal APIs), reasons over the result, and either finishes the task or hands off to a human. The work, not just the conversation.
- 02What does "production-grade" mean for you?
- Evaluated. Bounded. Observed. Every agent ships behind a golden eval set, sandboxed tool execution, audit trails on every step, and dashboards for cost, latency and task-completion. Changes pass evals before they roll out.
- 03How do you keep our data safe?
- Per-tenant memory isolation, configurable PII redaction, scoped API keys for every tool call, and the option to host on your cloud (AWS / GCP / Azure). We sign DPAs on request and align to your data-residency rules.
- 04How long does an agent take to ship?
- A focused single-workflow agent ships in 4–6 weeks including evals and shadow-mode. A multi-tool agent across several systems usually takes 8–12 weeks. We give a realistic estimate after the workflow-discovery call.
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