Sales research agents
Pre-brief every call with a synthesis of the prospect's 10-K, product releases, competitor wins, and the last six touchpoints — generated in under 60 seconds.
- CRM-native
- RAG pipeline
- Observability
Pillar 01 · Service 01 AI Automation
AI agents that actually do the job.
Custom agents and copilots built for the real work your team is doing — not the demo your vendor showed on stage. Trained on your data, wired to your stack, evaluated against your definitions of "good."
- Typical engagement 4–12 weeks
- Production-ready on day one Yes
- You own prompts, evals, logs Always
01 What we build
Five agent shapes,
tuned to your stack.
Every engagement maps to a specific workflow your team is stuck on — then evolves into a system that compounds.
Outreach copilots
First-touch emails grounded in the prospect's public signals — not "Hi {firstname}." Editor-in-the-loop by default; full autonomy once your rules are stable.
- Multi-channel
- A/B eval
- Reply triage
Support & success copilots
Tier-one resolution for your help center's long tail. Routes uncertain cases to humans with full context — never invents a policy that doesn't exist.
- Knowledge base
- Handover flow
- Tone control
Internal knowledge agents
An always-on assistant trained on your docs, decks, Slack history, and internal wikis. Answers the same twenty questions new hires ask every quarter.
- Slack / Teams
- Permissions aware
- Cited answers
Multi-step task agents
Agents that take a goal (generate this report, onboard this customer, triage this queue) and execute across multiple tools — with human checkpoints where they matter.
- Tool use
- Checkpoints
- Audit trail
Evals, monitoring & fine-tunes
The unsexy part that determines whether your agent still works in six months. Every engagement ships with an eval harness and a monitoring setup your team can extend.
- Offline evals
- Prod monitoring
- Drift alerts
02 Stack
Model-agnostic. Stack-native.
We pick tools based on what fits your data, your team's skill set, and your procurement rules — not what's on our partner slide.
ClaudeAnthropic
GPT-4 / o-seriesOpenAI
Llama / MistralOpen-weight
LangGraphAgent runtime
Pinecone / pgvectorVector stores
BraintrustEvals
LangSmith / HeliconeObservability
TemporalLong-running jobs
03 How we ship
Four weeks to a
production agent.
Map
Shadow the team doing the work today. Identify the decisions, the edge cases, and the parts a model will reliably fail at.
Spec
Prompt architecture, tool surface, data contracts, and the eval set we'll use to decide if it's working.
Ship
Agent goes live behind a feature flag. Instrumented from the start. Daily red-team review until the eval curves stabilize.
Tune
Failure modes become eval cases. Eval cases become prompt updates. Your team owns the loop by the end of week four.
KacificSatellite Internet · SG
X-PHY IncCybersecurity · US
FlexxonNAND Solutions · SG
Gauze CareMedical Products · US
NDAIndustrial SaaS · EU
NDAHealthtech · US
NDAClimate & Energy · APAC
NDAB2B Fintech · UK
Let's build it
Agent in mind?
Let's scope it.
Tell us the workflow you'd automate first. We'll send back a read on whether an agent is the right tool, and what production would actually look like.