Case studies from the real operation

Average document search time dropped from 12 minutes to 45 seconds.

Search success rate improved from ~60% to 95%.

The result is one reusable AI platform instead of a pile of disconnected pilots.

Service, L2 support, collections, and commercial flows can share the same orchestration, knowledge, and governance layer while still keeping channel-specific logic where it matters.

The result is one customer-operations platform instead of separate bots per brand or channel.

Voice, chat, L2 support, debt-related flows, and sales conversations can share routing, knowledge, references, and governance while still behaving differently where needed.

Fully operational private AI platform running inside client infrastructure with zero external data transfer.

Engineering teams using RAG-based document search across technical manuals and project archives.

The validation showed that a meaningful share of repetitive support volume can be taken out of the frontline queue without breaking the customer experience.

The agent resolved 38% of tested Tier-1 issues autonomously, classified intent with 91% accuracy, and produced structured escalation summaries that reviewers rated useful in 87% of handovers.

The pipeline gave the brand a workable route out of slow, inconsistent catalogue production.

Editors approved 72% of generated descriptions with minor or no edits, average generation time dropped to around 8 seconds per SKU, and brand-voice consistency improved from 3.2/5 to 4.1/5 in blind reviews.

The assistant cut the narrative drafting phase roughly in half for the tested topics and saved an estimated 3 to 4 days of writing time.

More importantly, it improved coverage discipline: the team started from a structure that stayed close to the underlying disclosure requirements instead of reconstructing them manually every cycle.

The workflow cut first-pass contract scanning time by roughly half and saved lawyers an estimated 30 to 45 minutes per document on the tested set.

That is meaningful leverage in a practice where review pressure is constant and senior attention is expensive.

The pilot group rated 84% of answers as useful or correct, and users found answers in under 30 seconds for questions that previously took 15 to 20 minutes of searching or internal escalation.

For the operation, that means less dependency on the same experienced colleagues and less time disappearing into procedural lookup work.

Extraction accuracy: 91% of fields correctly extracted across the test set of ~200 documents, including scanned and photographed originals.

Validation catches: The validation layer flagged 23 mismatches that would have been missed in manual processing during testing.

The workflow reduced dossier preparation time from roughly 3 hours to 45 minutes per client while keeping traceability back to the underlying sources.

That is a material operational gain in onboarding and periodic review work, where analyst time disappears quickly into repetitive preparation.

The workflow reduced initial spec review from roughly 2 hours to 15 minutes per document on the tested set.

Engineers still verify the outcome, but they start from a structured comparison instead of a raw PDF hunt.

Bid managers rated the generated drafts as a usable starting point rather than a novelty output: 3.8/5 on average versus 4.5/5 for the original manually written winning submissions.

First drafts arrived in around 12 minutes instead of 2 to 3 days, and the retrieval layer selected the right supporting material in 4 out of 5 tenders without manual correction.

On the tested Woo set, the pipeline reached 89% recall on PII entities, 78% precision on proposed redactions, and reviewers reported working about 2.5 times faster than in the manual process.

That matters because Woo work is not just high-volume, it is politically sensitive and operationally draining.