Know where you stand. Know what to fix. Lattice scores, interviews, and remembers so every attempt makes you stronger.
Built on a production-grade,
multi-agent stack.




Career prep isn’t a checklist. Agents work in parallel and what they learn carries into your next application.
TinyFish
Hugging FaceEMBEDDINGS
LangGraph
Jev
TokenPathRAG
Groq
OpenRouter
JevORCHESTRATOR
Pipecat
LiveKit
Cartesia
NotionCAREER MEMORYLattice remembers what you learned, what you missed, and what you need to improve — so every goal starts with more context than the last.
Every skill in your profile carries the sentence and section it came from. Anything unsupported gets flagged as missing evidence rather than quietly asserted.
Three independent specialists score you concurrently on different dimensions. When they disagree, the outlier is re-run once with the others’ reasoning before anything is finalized.
Live voice interviews are transcribed in full. Evaluation is derived from the transcript by a dedicated specialist, never a hardcoded rating.
Confirmed weaknesses from each interview roll forward into your candidate profile. They remain reviewable and are never written without an evaluation behind them.
Your resume is chunked, embedded locally and retrieved per question, so the chat answers only from your own evidence.
Your resume shows backend experience through Django API development and PostgreSQL data pipelines.
Three perspectives. Zero guesswork. Understand every score, every disagreement, and your fit across 11 dimensions tailored to the job.

Eleven weighted dimensions, not an average. One missing required skill visibly caps the score, and every bar comes from semantic similarity against your real resume chunks.

Drafted from the job profile plus your evidence, specific to the company. Never sent without your review. Export only after you approve.

Aggregated from Arbeitnow, Remotive, Jobicy, and RemoteOK, deduped, then ranked by semantic similarity and skill overlap against the role you care about.
Gemini Live or Pipecat on the other side. Interrupt mid-sentence and it responds naturally, with a plan weighted to the role and evaluation derived from what you actually said, not a template.
Confirmed weaknesses roll forward. Reviewable, and never written without an evaluation behind it.
Reads yesterday from your own data: readiness moves, deadlines closing, and roles worth a look. Then it writes the one paragraph that matters.
Ask in plain language. It searches live restaurants and real menus through MCP, then proposes an order with actual prices.
Or skip the chat: real menus, a veg filter, and a live Swiggy cart with the server’s own fees and taxes, kept in its own tab.
The cart and the payment methods are genuinely yours. Placing the order is real. Let agents do the rest.
Because the second a machine can apply for you, you inherit everything it got wrong, at the speed it can send. A bad match, a hallucinated claim, a recruiter who now remembers your name for the wrong reason.
Every write pauses the graph on a real interrupt, not a confirm dialog bolted on afterwards. Approve it, or reject it with a reason the agent reads before it tries again. There are eight of these moments, and none of them is “submit application”.
Draft ready: 3 leads, public context only. No warm intro is implied.
Outreach is drafted, never dispatched: there is no send path in the codebase to begin with. You copy the message, send it from your own account, and mark it sent, so the record matches what actually happened.