AI Development
Most AI projects die in the demo. Yours gets to Friday.
The last step is a person. Nothing sends on its own.
We’ve built these enough times to stop estimating them.
Every engagement is custom, but the underlying patterns repeat. These are the surfaces where AI consistently moves the needle for marketing and operations teams.
Custom GPTs & assistants
Branded, persona-driven assistants trained on your voice, your data, and your guardrails. For support, sales, copywriting, and ops.
- Tone-locked outputs
- Auth-gated access
- Tool / function calling
RAG & knowledge systems
Search your handbook, your contracts, your past creative, and get a real answer with citations, not a hallucination.
- Vector + hybrid search
- Source citations
- Slack / web / API surfaces
Workflow automation
Long, repetitive marketing ops (brief generation, asset routing, reporting) replaced with deterministic pipelines.
- n8n / Make / Zapier
- Python + LangGraph
- Human-in-the-loop checkpoints
Voice & phone agents
24/7 inbound + outbound voice agents that book, qualify, and escalate. With a human-quality script and call analytics.
- Vapi / Retell / Twilio
- CRM write-back
- Transcript scoring
Fine-tuning & evals
When prompting plateaus, we fine-tune. With proper eval suites so you know when the model is actually getting better.
- OpenAI / Anthropic / OS
- Eval harnesses
- Cost-quality tradeoff plans
AI strategy & audits
Where does AI actually belong in your stack? We map opportunity, risk, and ROI, and tell you what not to build.
- Workflow inventory
- Build / buy decisions
- Governance & data policy
From whiteboard to production in five.
A repeatable engagement model that gets you to a working system fast, without skipping the boring parts that make it actually work in 6 months.
Map the problem
Workflow walk-through, data audit, success metrics agreed before any prompt is written.
Spike, don’t sprint
A scrappy v0 in days, not weeks. Real data, real users, real feedback.
Production build
Auth, logging, evals, fallbacks, rate limits: the unsexy 80% that keeps it alive.
Wire to your stack
Slack, Notion, HubSpot, your site: wherever your team already lives.
Tune & observe
Weekly evals, cost dashboards, prompt versioning. AI that gets better, not flakier.
We’ll use whichever model doesn’t wake you at 3am.
We don’t have a favorite model. We have favorite outcomes. The stack flexes to the job, sometimes that’s a $0.0001 prompt, sometimes a fine-tuned 70B with a vector store and a queue.
- ModelsOpenAI, Anthropic, Google, Mistral, Meta Llama 3, plus open weights for sensitive data.
- OrchestrationLangGraph, LlamaIndex, custom Python: whatever survives a 3am pager.
- Vector & dataPinecone, Weaviate, pgvector, Supabase, BigQuery: driven by your data residency.
- SurfacesSlack, Notion, HubSpot, Webflow, custom dashboards: your team’s existing tools first.
- ObservabilityLangSmith, Langfuse, custom evals, because you can’t improve what you can’t measure.
from langgraph.graph import StateGraph
from tools import rag_search, brand_tone, slack_send
def build_agent():
g = StateGraph(BriefState)
g.add_node(“research”, rag_search)
g.add_node(“draft”, brand_tone)
g.add_node(“review”, human_in_loop)
g.add_node(“deliver”, slack_send)
return g.compile()
# emitted per run: latency, token cost, and CSAT
Real systems, for teams that have to use them Monday.
A few of the patterns we ship most often. Each one started as “wouldn’t it be great if…” and ended as a button in someone’s Slack.
Brief & deck generator
Strategy team feeds in a goal + brand context, gets back a media plan, creative brief, and pitch deck: pre-formatted, on-tone, ready to edit.
Inbound voice qualifier
24/7 voice agent answers leads, qualifies BANT, books to calendar, writes the recap to HubSpot: handing off only the hot ones.
Knowledge-grounded helpdesk bot
Trained on your help center, past tickets, and product docs. Cites sources. Escalates with a transcript when it’s out of depth.
SEO content factory
Keyword cluster → outline → draft → editorial review queue. With brand voice locked in and human approval before publish.
The toolkit, opinionated.
Tools we reach for first. Always evaluated against the alternative.
Three ways to do this. One is cheaper than you’d guess.
From a tight discovery sprint to a long-running embedded squad. Pick the door that matches your readiness. We’ll help you decide on a free 30-minute call if you’re not sure.
AI Audit
Map your workflows, score them for AI fit, and walk away with a prioritized roadmap of what to build, buy, or skip.
- 2-week engagement
- Workflow inventory + scoring
- Build / buy recommendations
- 30-page roadmap deliverable
- Final readout with leadership
Build Sprint
A focused 4–8 week build for one specific surface. A custom GPT, a RAG assistant, a voice agent, or an automation pipeline.
- Discovery + data prep
- Production build & deploy
- Eval suite & observability
- Team training + handoff docs
- 30 days of post-launch tuning
Embedded Squad
For teams shipping AI as a real product line. A senior AI engineer + strategist embedded month-to-month with your stack.
- Senior AI engineer + PM
- Multiple shipping streams
- Eval & cost dashboards
- Quarterly model refresh
- SLA & on-call options
Questions we get asked, including the rude ones.
If yours isn’t here, drop a note. We answer every inquiry inside one business day.
Will my data train someone else’s model?
No. We use enterprise endpoints with zero-retention contracts wherever possible (OpenAI ZDR, Anthropic, Azure OpenAI). For sensitive data, we deploy open-weight models on infrastructure you control. Data policy is part of the audit, not an afterthought.
How long until something is in production?
For a Build Sprint, expect a working v0 in 1–2 weeks and a deployable system in 4–8. We resist the consultant move of “we’ll have a strategy deck for you in Q3”, momentum is the project.
Can you work with our existing AI / engineering team?
Frequently the best way. We pair with internal teams as a force multiplier. Bringing patterns, tooling, and prompt engineering depth, then handing off cleanly. No black-box code.
What if the model “gets dumber”?
Eval suites and prompt versioning are built into every project. When a model regresses (it happens), we catch it on the dashboard. Not from a customer ticket. Quarterly model refresh is part of the embedded retainer.
Do you handle compliance / SOC 2 / HIPAA?
We’re not a compliance vendor, but we build to your control set. We’ve shipped systems inside SOC 2 Type II, GDPR, and HIPAA-adjacent environments. We’ll route data and choose models accordingly.
What does “production” mean to you?
Auth, logging, evals, fallback paths, rate limits, cost monitoring, prompt versioning, on-call runbook, and a human-in-the-loop where stakes are high. If it’s not there, it’s a demo, not a system.
Everyone’s seen the demo. Fewer have shipped the system.
Tell us the workflow you’re tired of doing by hand. We’ll come back with a 1-page plan and a price. Free, no slides, no obligation.
Send a quick brief.
Tell us what you’re working on. We’ll read it within a business day and reply with honest next steps.