Problem Solution Stats Capabilities FAQ
AI sales intelligence · Built for technology services

Sell with context. proof. confidence. speed. sources.

Auris helps technology services sales teams find the right experts, surface relevant case studies, and assemble proposal teams from approved internal data — inside Microsoft Teams workflows and on customer-controlled infrastructure.

Join the waitlist See the numbers
Teams-native · Source-grounded · Human-approved · On-premise-first
Talent intelligenceCapability discovery Case study retrievalProposal team builder Proposal teams in secondsRanked by skill, domain, history Your data stays on your infraSource-cited on every answer Talent intelligenceCapability discovery Case study retrievalProposal team builder Proposal teams in secondsRanked by skill, domain, history Your data stays on your infraSource-cited on every answer
The problem

Deals stall between the question and the proof.

Your firm has the people, the history, and the evidence buyers ask for. The problem: it's scattered across CVs, case studies, decks, SOWs, drives, and chats.

01
Expertise nobody can find fast enough
Sales teams need to know who has the right skill, industry experience, and project history before the proposal window closes.
People
02
Proof buried in old repositories
Relevant case studies, SOWs, RFP responses, and decks exist, but reps often cannot find the best evidence during live deal work.
Content
03
Proposal teams built by memory
Assembling the right team means chasing delivery leads, HR, and operations instead of starting from a source-backed team draft.
Coordination
04
Generic AI cannot own sensitive context
Horizontal assistants help with generic drafting, but they're not built for services-firm staffing, capability proof, or strict data-sovereignty.
Trust
The solution

One question. One source-grounded answer.

Auris indexes approved internal sources and turns them into a sales intelligence assistant. Reps ask in plain English. Auris retrieves the evidence. Humans decide what goes to the client.

Four workflows
QUERY
QWho knows Snowflake and has fintech delivery experience?
RESPONSE · ranked matches
MR
Maya Reed — Data Architect
Snowflake · fintech migration · 2 projects
High
OA
Omar Ali — Senior Engineer
ELT pipelines · banking analytics
Strong
LC
Lina Chen — Delivery Lead
Data programs · regulated clients
Good
CLIENT QUESTION
A prospect asked if we do React Native. Do we?
SOURCES FOUND
Mobile case studyCapability deckEmployee profiles
Yes — Auris found recent cross-platform mobile work, the delivery team involved, and source material a rep can review before replying.
Case study match
Ranked from approved sources
NeedLegacy platform modernization
MatchCloud migration + integration case study
ProofDeck, SOW excerpt, delivery team profiles
UseReview shareability before client send
Fintech transformation · team draft
Review
Engagement lead with fintech delivery history
Solution architect matched to integration scope
Data engineer with Snowflake project proof
Refine: swap senior role, add QA, or filter by availability
Product demo

See it in action.

Watch how a rep asks a question in Teams and Auris surfaces source-grounded answers from approved internal data in seconds.

Teams-native workflow
Source-grounded answers
Human approval gate
auris · teams · #proposal-intelligence
DEMO
Auris product demo preview
Watch the demo
By the numbers

Hours back. Deals out the door.

Mid-market services sellers lose 20–40% of cycle time to internal forensics — hunting CVs, chasing case studies, pinging delivery leads. Auris reclaims it.

0hrs
Recovered per seller / week
Typical reclaim per SDR/AE — 6 to 8 hours every week lost to internal search.
min →0sec
Answer time on staffing questions
The 30-minute Slack thread compressed to seconds.
0%
Internal-search waste recovered
Customer-value range of 60–80% reclaim against the existing forensics tax.
0%
Source-grounded accuracy
Capability answers benchmarked against what your team would say manually.
0wks
From contract to live in Teams
6–8 weeks to a validated pilot. Half a day from IT. 30 min training per user.

Sources: customer-value math from Auris market analysis (5–25 sellers × 10–15 hrs/week × $80–120/hr fully-loaded time). Accuracy and cycle figures from product strategy benchmarks. Individual results vary by data quality and rollout scope.

Capabilities

Four core capabilities. One proposal workflow.

Deliberately narrow: internal sales intelligence for services firms, not a generic chatbot, CRM, or autonomous outbound tool.

01 · Talent Intelligence

Find the right people

Search approved employee profiles and CVs by skill, domain experience, project history, recency, and availability.

People + project proof
02 · Capability Discovery

Answer "have we done this before?"

Surface real project history instead of relying on stale marketing copy. Answers point to reference projects and relevant experts.

Source-backed capability answers
03 · Case Study Retrieval

Find the proof behind the claim

Retrieve similar case studies, SOWs, decks, and proof points with source links a rep can verify before using externally.

Case studies + SOWs + decks
04 · Proposal Team Builder

Draft a proposal team

Convert a project brief into a role-based team draft with fit reasoning, project-history matches, and conversational refinements.

Highest-ROI workflow
Built into every workflow
Built in · Refinement

Refine in conversation

Follow up naturally: ask for the third profile, swap a senior role for mid-level, add QA, or narrow results to a specific practice or region.

Multi-turn refinement
Built in · Trust

Keep humans in control

Auris is designed to cite sources where available, flag uncertainty, and keep client-facing use under human review instead of autopilot.

Confidence-aware by design
Beyond search

It learns the way your team sells.

Live · Improving daily

Every thumbs-up tunes the answer.

Auris ships with a three-layer semantic cache and a feedback-driven scoring loop. When a seller confirms an answer, the system retains that retrieval pattern longer and ranks it higher next time.

The result: an assistant that on day one understands your CV corpus, and by month three understands how your team actually pitches.

  • Three-layer semantic cache: similarity match, chunk reuse, and feedback-weighted TTL — sub-50ms on hot answers, sub-1.5s on cold.
  • Confidence-aware routing. When uncertain, Auris asks a clarifying question instead of guessing.
  • Routing-decision audit trail. Every retrieval, every rerank, every fallback recorded for compliance.
Feedback loop · live signals
01
Seller asks a staffing question
in Teams
02
Auris retrieves + ranks candidates
~1.2s
03
Seller confirms or refines
human signal
04
Score updates · TTL tunes · rank shifts
async
05
Next similar question · sharper answer
compounding
Latency · measured
Cache hit (L1)~44 ms
Cache hit (L2)~10 ms
Cache miss target< 1.5 s
Shipping next

A research agent that drafts with proof.

Standard SDR tooling sends sequenced emails. Auris does the work before the email — researching the prospect, matching the right case studies, and drafting an opener that names specific delivery you've actually done.

  • Prospect enrichment from public web — recent news, role changes, tech stack mentions, funding events.
  • Auto-matched case studies and team profiles from your approved internal data, with source links.
  • Hard-coded human approval gate. No outreach ever sends without explicit seller confirmation — non-configurable.
Draft · ready for review
Acme HealthcareVP Sales3 proof points attached

Subject: Snowflake architects for your Q3 fintech build

Hi Anita — Saw your post last week on the Acme data-platform expansion. We've staffed two similar Snowflake + regulated-data engagements in the last 18 months — happy to share the case studies.

Specific question: when your team needs to identify the right architect with both Snowflake depth and healthcare delivery history, how long does it currently take internally?

20 minutes next week worth your time?

Sources: 2 case studies · 4 CVs · 1 SOW excerpt
On the roadmap

Pipelines know what you pitched.

Auris is built to plug into where deals already live. Pull opportunities from your CRM, attach the proposed team and supporting case studies back onto the deal record, and surface staffing decisions where the rest of the org can see them.

  • Bidirectional sync: deals in, proposed teams + case-study attachments out, on the opportunity record.
  • Triggers off pipeline stage — a deal moves to "proposal" and Auris pre-stages the staffing answer before the seller asks.
  • Designed to coexist with existing CRM AI features — Auris owns internal intelligence, the CRM owns the deal.
Supported integrations
Salesforce
Sales Cloud · Einstein-coexistent
HubSpot
CRM + Sales Hub
Dynamics 365
Native Microsoft stack
Flow · pipeline-driven
Deal moves → Proposal stagetrigger
Auris drafts team + attaches proof~30s
Posted to deal recordauto-log
Seller refines in Teams→ pushes back
In practice

Real questions. Proposal-ready workflows.

These are the kinds of questions Auris is designed to answer from a firm's own approved data during sales and proposal work.

Question asked in TeamsWhat Auris returns
"Do we have anyone who knows Snowflake and has fintech experience?"Ranked people · skills, project proof, availability where available
"A prospect asked if we do React Native. Do we?"Capability answer · reference projects and relevant experts
"Find a case study similar to this modernization opportunity."Ranked examples · source links and summaries for review
"Build a proposal team for a 3-month transformation engagement."Role-based team draft · rationale and refinement options
Implementation

From sample data to a validated pilot.

STEP 01

Start with approved data

Use an approved or anonymized sample of employee profiles, CVs, case studies, SOWs, and decks to validate retrieval quality before expanding scope. In production, Auris indexes directly from your connected sources.

STEP 02

Configure the sales vocabulary

Map customer terminology, capability taxonomy, role names, brand language, source permissions, and review expectations to the firm's workflow.

STEP 03

Pilot inside the sales workflow

Bring Auris into the team's Microsoft Teams workflow during implementation, start with a focused user group, and expand after validation.

Trust & control

AI assistance. Human accountability.

Auris is built for client-facing sales work where the cost of a plausible but unsupported answer is too high. The system is designed to retrieve, cite, flag, and ask — not invent.

Human-approved outputs

Auris assists the rep. Anything used externally should be reviewed by a human before it reaches a client or prospect.

Source-grounded answers

Responses are grounded in indexed sources where available. If Auris cannot find support, it flags uncertainty or asks for clarification.

On-premise-first deployment

Core retrieval and local model inference are designed for customer-controlled infrastructure. Deployment scope defines any optional external services.

Configurable to each firm

Auris can be configured around the customer's terminology, capability taxonomy, partner brands, prompt templates, and sales workflow.

Sales teams stay in control

The product reduces internal search and coordination work. It does not replace seller judgment, relationship ownership, or delivery validation.

Auditability path

Routing decisions, retrievals, and responses can be logged for review. Customer-facing audit reports and dashboards are part of productization.

Product readiness

Current strengths. Clear boundaries.

Auris is intentionally positioned around the capabilities supported by the product strategy: internal expertise, internal proof, and proposal team assembly.

01
Core workflows
Talent intelligence, capability discovery, case study retrieval, proposal team building, and conversational refinement are the core demo narrative.
Current focus
02
Enterprise foundations
On-premise deployment, configurable terminology, internal audit logging, and Teams-based identity provide the enterprise direction; productized dashboards and install automation are being hardened.
In progress
03
Implementation model
A tailored demo can use approved sample data. Production rollout depends on data quality, infrastructure access, source onboarding, and pilot validation.
Scoped rollout
04
Roadmap items
CRM bidirectional sync, predictive staffing, win-pattern analysis, white-label modes, air-gapped validation, and full customer-facing dashboards belong on the roadmap until productized.
Roadmap
Common questions

Answers, up front.

Everything you'd want to know before starting a conversation with us.

Auris is not a general-purpose assistant. It is built for technology services sales workflows: finding internal experts, proving capabilities from real project history, retrieving case studies, and assembling proposal teams from approved internal data.
Auris is designed around customer-controlled, on-premise-first deployment. The core data plane runs on customer infrastructure, and the sources indexed are limited to approved repositories.
Auris is designed to retrieve from indexed sources, show source context where available, flag low-confidence results, and ask clarifying questions when a request is ambiguous. Client-facing use should remain human-reviewed.
A tailored demo can be prepared with approved or anonymized sample data. A production rollout typically takes 6–8 weeks depending on data quality, infrastructure access, the number of repositories being onboarded, and pilot validation with a focused user group.
Auris is built for mid-market technology services firms where sales velocity depends on quickly finding the right people, proof, and proposal team. It fits teams large enough that internal knowledge is fragmented, but focused enough that a high-touch implementation makes sense.
Auris is not a CRM, not a proposal-writing platform, not a generic document Q&A tool, and not an autonomous outbound sales system. It is sales intelligence for services firms that need to find their own people, proof, and delivery history faster.
CRM bidirectional sync, predictive staffing, win-pattern analysis, white-label modes, fully productized customer dashboards, and validated air-gapped deployment should be treated as roadmap items until scoped for a specific customer.
Design-partner pilots · Limited intake

Stop losing deals to the search bar.

Auris is opening a small cohort of design-partner pilots for mid-market services firms. Get on the list — we'll share the pilot scope, the on-premise deployment plan, and walk through what reclaiming 6–8 hours per seller per week looks like for a team your size.

You're on the list. We'll be in touch shortly with pilot scope.
Replies from a real human · No sales spam, ever