GenAI implementation · Enterprise teams

GenAI implementation, done properly.

We design and build AI systems for enterprise teams — documents, reporting, internal knowledge, and data & analytics QA. Built in your environment, documented fully, handed over to your team.

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Document intelligence Reporting automation Knowledge systems Data & analytics QA

What we build

Document intelligence

Extraction and routing for invoices, contracts, and intake documents. Structured data out of unstructured paperwork, with human review on anything that matters.

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Reporting & analysis

Automation for recurring reports: data collection, validation, and first-draft narrative. Your analysts review and decide instead of gathering and formatting.

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Knowledge systems

Internal systems that answer questions from your own policies, SOPs, and documentation — with sources cited, so answers can be checked.

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Data & analytics QA

Agents that test your data end to end — validate pipelines, verify tracking, triage failures, and file tickets to the owning team. Our deepest specialty.

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How we work

OneTPM comes out of years spent building agentic automation inside enterprise teams: data-quality operations, analytics systems, customer-feedback pipelines.

That work sits under NDA, so we let the method carry the weight. Our approach, our architecture patterns, and a sample of the audit document every engagement produces are all public on this site. Read them before you talk to us.

Adoption is scoped in

A system your team doesn't use is a failed project. Training and rollout are part of every build, not an add-on.

Everything is documented

Architecture, prompts, runbooks. You get all of it. There is nothing we could hold back to keep you dependent.

Handover is the goal

Engagements end with your team operating the system on their own. We measure ourselves against that.

A few of our projects

Data quality · Enterprise analytics

Automating data-quality triage and bug filing

Situation: A data-quality pipeline fed a scorecard dashboard. Analysts across teams checked it daily, investigated failures by hand, and filed and assigned tickets — slow, tedious, and error-prone.
Build: An agent that queries the test-results table directly, analyzes each failure against the source tables, then files, prioritizes, and routes Jira tickets to the owning team.
Result: 98% routing accuracy, over 100 analyst-hours returned per week across teams, and issue-resolution time down 40%.

Test automation · Consumer media

End-to-end analytics testing from a plain-language instruction

Situation: Verifying interaction analytics meant manually browsing the product while watching network logs — pageviews, scroll depth, clicks, video events — then tracing the same data through raw, fan-out, and semantic layers. Coding the automation for one journey took an SDET about a week.
Build: An agentic framework that takes an instruction like "visit the homepage and verify pageview events fire with these attribute values," drives the client, validates the events, traces them through every backend layer, and writes results and a full audit trail to tables.
Result: A week of SDET work per journey became a sentence of instruction, with every run leaving an auditable trail.

Analytics · Cross-platform

An agentic analyst over the organization's data

Situation: Data questions from across the business queued behind analyst availability. Simple questions waited days; many were never asked.
Build: An agentic data analyst anyone could question directly. It finds the right dataset, writes and executes the queries, and answers with charts.
Result: Routine data questions stopped requiring an analyst. Analyst time moved to the questions that actually needed one.

Customer feedback · Consumer apps

Classifying 400,000 monthly product reviews at full coverage

Situation: Product reviews arrived at roughly 400,000 a month across app stores and platforms, classified and prioritized by hand. Manual capacity covered about 15%, so urgent signals — like a core feature failing for paying users — surfaced late or not at all.
Build: An agent pipeline that classifies every review into meaningful buckets, scores priority, and routes high-priority feedback directly to the team that owns the affected feature.
Result: Coverage went from 15% to 100% with better accuracy. High-priority issues now reach the owning team immediately.

Production intelligence · Content studio

Turning scattered production knowledge into a learning database

Situation: At a high-velocity content studio, what the organization learned lived in meeting notes, Slack threads, and tickets. Feedback repeated across projects, lessons were relearned, and the people who remembered were the only search index.
Build: A pipeline that ingests the scattered sources, titles and summarizes each item of feedback, and files everything into a structured long-term learning database teams can actually query.
Result: What was learned once stayed learned. Decisions and feedback became a searchable asset instead of tribal memory.

Next entry · Unwritten

This entry is reserved for your process.

Bring the work that consumes your team's week to a 45-minute working session. We'll map it with you and tell you honestly what a build would take.

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Writing

Architecture RAG architecture for enterprise: what actually works in production 14 min Agents The human-in-the-loop pattern: where AI needs a checkpoint 11 min Deployment The real cost of running LLMs in production 11 min

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Start with one process.

A 45-minute working session. We map one process with you, estimate what automation would take, and give you our honest read — including when the answer is "don't automate this." You keep the notes either way.

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