Deflect, assist, escalate — with AI that knows your policies.
We build AI support systems that sit on your real policies and tools, lifting CSAT while cutting AHT and cost per contact.
From RAG on your knowledge base to agent-assist copilots and full self-service deflection — grounded, auditable, and tuned to your SLAs.
Trusted by teams at
The mandate
Run a support operation where AI handles what it should, and humans handle what matters.
Not every ticket deserves a human. We classify, deflect, and resolve where policies are clear; and we copilot your agents where judgment is required — always with a trace, always with escalation.
What you get
- Self-service AI trained on your actual policies, not generic FAQs.
- Agent-assist copilot inside your helpdesk (Zendesk, Intercom, Salesforce).
- RAG pipeline over policies, macros, knowledge articles, and past tickets.
- Smart routing, intent classification, and escalation criteria your ops team owns.
- Metrics: AHT, FCR, SLA, CSAT, deflection rate, contained cost per contact.
Why it works
Why this approach wins.
01 · Principle
Deflection that doesn't infuriate users
We tune the bot to hand off fast and silently when it's unsure. Deflection rates go up; complaints about bots go down.
02 · Principle
Agent-assist that your agents actually use
Inline suggestions, policy lookups, and tone help — embedded in their workflow, not a new window. Adoption is the KPI.
03 · Principle
Policy changes propagate in hours
Update your knowledge source; the AI reflects it within a fresh retrieval cycle. No retraining, no prompt-patching.
Outcomes
The outcomes we commit to.
40–60%
deflection on L1
−35%
avg. handle time
+12pt
CSAT lift
3 wks
to first pilot
Awards
Proud moments.
Top 1% on Clutch Global
Top AI Strategy Company 2025
Clutch Fall Champion 2024
Inc. 5000 Fastest Growing
Breakthrough of the Year 2019
Member of Forbes Tech Council
Voice & Speech Recognition 2024
Top Blockchain Company 2024
Innovators of the Year 2019
GoodFirms Top Company
Pain points
Do you recognize your team?
What's happening
- Ticket volume spiking faster than hiring can keep up.
- A CX leader asked: “why don't we have an AI for this?”
- BPO costs ballooning; CFO wants a plan.
- Agents quitting from repetitive L1 queues.
How it feels
- Burnt out from firefighting seasonal spikes.
- Anxious about bot failures ending up on social media.
- Embarrassed by the gap between your AI demo and your live IVR.
- Protective of CSAT — and scared to risk it.
Where it hurts
- Hallucinated answers in customer-facing channels.
- Knowledge base rot — bot quotes outdated policies.
- No easy way to measure containment vs. escalation.
- Agents ignoring copilots that add friction.
- Vendor-lock to a single contact-center AI stack.
What we ship
Workstreams, real artifacts, measurable outcomes.
Every engagement decomposes into clear workstreams you can ship and measure. Here's the playbook for this segment.
01
Self-service deflection
- Intent taxonomy
- Policy-grounded bot
- Handoff rules
- Dashboards
02
Agent assist copilot
- Copilot UX
- Policy retrieval
- Macro suggester
- Adoption tracking
03
RAG on policies
- Ingestion pipeline
- Retriever
- Freshness SLA
- Audit trail
04
Metrics & ops
- KPI dashboard
- Eval sets
- QA sampler
- Weekly ops cadence
As seen in
After-state
What changes on the other side.
Self-service handles the easy half; agents handle the hard half faster. Every answer is policy-grounded and traceable. Scaling season doesn't mean panic hiring.
How it feels
What becomes possible
- 01Absorb a 2× volume spike with the same headcount.
- 02Re-allocate senior agents to retention and upsell.
- 03Turn support data into a product-feedback engine.
Concerns, answered
The usual concerns — handled.
Concern 01
“Our customers will hate talking to a bot.”
Ours hand off fast and silently when unsure. We tune thresholds on your data and show you a full trace of every AI-only resolution.
Concern 02
“Our knowledge base is a mess.”
Fine — cleaning it is part of the engagement. We'll flag conflicts, gaps, and deprecated answers as a byproduct of building the retriever.
Concern 03
“Legal won't let AI answer anything.”
We start with read-only agent-assist — no customer-facing risk. Once legal sees the traces and guardrails, customer-facing scope expands.
Concern 04
“We already pay for Zendesk AI.”
Great — we'll audit what it's actually catching. Most teams need a layer on top, not a replacement.
Alternatives
Why us and not…
Zendesk AI / Intercom Fin
Strong baselines; weak on custom policies, audit, and pain-point tuning.
In-house bot teams
Heroic but slow. We bring the playbook and the evals.
Traditional BPOs
Linear scaling by headcount. We un-link volume from cost.
Case studies
Where ideas become impact.
Behind every system we ship is a team that moved from uncertainty to measurable outcomes. A few recent ones.
Case 01 · Client
Wealth Management Company
Objective
The goal was to integrate AI tools into everyday work across all roles and increase overall productivity.
Results
85%
of employees use AI tools daily in workflows
70%
of routine queries resolved via GPT assistant within the first 2 weeks
5 min
Average response time reduced from 1 hour to 5 minutes
52
ready-to-use prompts created for key scenarios (finance, presale, legal, HR)
12
AI agents deployed for quality, sales, finance, and executive dashboards
100%
prompts reviewed for data security compliance
Stack
ChatGPT Enterprise, n8n, Cursor, RAGDB (vector database), Power BI + Bloomberg GPT, Miro, Whisper / Coqui
Case 02 · Client
E-Commerce Platform
Objective
Automate customer support and optimize product recommendation systems using AI.
Results
60%
reduction in customer support tickets
3x
increase in product recommendation conversion rate
24/7
Automated support coverage with AI chatbot
8
custom AI workflows deployed across departments
40%
faster content generation for marketing campaigns
95%
customer satisfaction score with AI-assisted support
Stack
Anthropic API, LangChain, Pinecone, Next.js, Vercel, PostgreSQL, Redis, NanoClaw
Founder & team
Senior humans,
AI-native craft.
100+
people trained
20+
companies transformed
9.4/10
avg. workshop rating
96%
AI adoption in 7 days
Talk to the founder
Mike Doroshenko
Product strategist and AI consultant with 10+ years of digital product strategy and AI transformation. Author of corporate training programs used by leading companies.
Supported by 30+ experts
from McKinsey, Google, and top tech companies.
Testimonials
Our clients said it best.
Patrik Dvořák
CEO, SECTOR 31 s.r.o.y
“Vahue's responsiveness and accuracy were impressive. We highly recommend them”
Philipp Lenz
Co-Founder, parloo.de
“There are a lot of companies that offer similar services but we've had an end-to-end good experience with them.”
Patrik Dvořák
CEO, SECTOR 31 s.r.o.y
“Vahue's responsiveness and accuracy were impressive. We highly recommend them”
Jacob Berg
CTO at Social Curator
“I appreciated the level of comfort Vahue made us feel. It was like being a part of a family.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the Vahue team was very impressive.”
Prasanna Elvis Eswara
Principal Consultant, Roost Digital
“They were proactive and seemed eager to build a relationship.”
Jacob Berg
CTO at Social Curator
“I appreciated the level of comfort Vahue made us feel. It was like being a part of a family.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the Vahue team was very impressive.”
Prasanna Elvis Eswara
Principal Consultant, Roost Digital
“They were proactive and seemed eager to build a relationship.”
Bartek Czerwinski
CTO, Quik
“Vahue has the ability to dive in and get the work done creatively with a lot of personal input.”
Steinar Aas
CEO & Co-Founder at Asio AS
“Their flexibility and genuine interest in finding the best solution for the product was impressive.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the team was very impressive.”
Bartek Czerwinski
CTO, Quik
“Vahue has the ability to dive in and get the work done creatively with a lot of personal input.”
Steinar Aas
CEO & Co-Founder at Asio AS
“Their flexibility and genuine interest in finding the best solution for the product was impressive.”
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