SocialScore's Customer Intelligence Layer gives AI service companies — chatbot vendors, support automation platforms, and marketing and sales tools — the real customer context their models are missing. It personalizes chatbot responses, reduces false ticket deflection and hallucination risk, and distinguishes real human shoppers from legitimate AI shopping agents and malicious bots, routing each to the right experience. Built for B2C consumer-facing AI services. Pricing starts at about €1 per verified profile, with volume pricing above 10,000 checks a month. A free demo and 500 free identity checks are available from SocialScore (DSNTech LTD, Sofia, Bulgaria).
You've automated the process.
You still don't know who's on the other end.
The Customer Intelligence Layer that tells your chatbot, your support AI, and your checkout who they're actually talking to — human or bot — before they respond.
Your AI is only as good as
what it knows about the person it's talking to
Without real context, an AI system doesn't personalize — it guesses. And guessing at scale looks like spam to a real customer, and like a hallucination when the model fills the gap with something it made up.
Same script for a first-time visitor and a loyal repeat buyer — generic answers where a good sales rep would already know the difference.
A ticket "deflected" without context isn't resolved — the customer comes back angrier, through a different channel, and the dashboard still looks green.
The same offer, blasted to everyone, reads as spam to the people it wasn't meant for — and misses the ones it was.
AI shopping agents are already buying — and most checkouts can't tell them apart from a human, or from a bad bot pretending to be one.
Everyone optimizes the system.
Only we optimize the knowledge of the customer.
Every AI vendor you talk to will improve the model, the workflow, the latency, the uptime. Almost none of them can tell your system who it's actually talking to — and that gap is where personalization quietly turns into spam.
Systems get optimized
Faster models, better prompts, cleaner pipelines. Every AI vendor sells you this. It's necessary. It's not the gap.
Context is the gap
Without knowing who's on the other side, your AI has to guess — and a guess dressed up in fluent language is what a hallucination actually is.
Context is what we add
300+ real signals about the person — or a clear signal that it isn't a person at all — feeding your system before it responds.
Your model knows language.
We tell it who it's talking to.
SocialScore enriches an email, phone, or session into 300+ external signals — the context layer that sits in front of your chatbot, your support AI, or your checkout, before either one has to guess.
Data & Signals Engine
Email + phone + session → 300+ signals, under 5 seconds. Your own product usage data can feed it too — not just email and phone.
Software & Capabilities
Works on our signals — or entirely on your own data: conversation history, purchase history, your own segments. Use us purely as software, if that's all you need.
Your chatbot, support platform, and checkout stay exactly where they are. SocialScore feeds the context they were missing — it never replaces the AI you already built.
From an email address to
a verified customer profile
Define the group
Chatbot visitors, ticket submitters, checkout sessions — any audience you choose.
Enrich & verify
300+ signals per identity, under 5 seconds. Batch mode for full lists.
Segment & predict
Behavioral segments matched automatically from external signals.
Act in real time
Every new visitor matched against your segments before your AI responds.
No devs required —
this is the actual product
Everything below is live in the SocialScore dashboard — no integration needed to see it working.
One call returns
nine different kinds of answers
Human or Bot
Real human, legitimate AI shopping agent, or a bad bot — the signal your checkout and your chatbot need most.
Verification
Deliverable, disposable, or breach-exposed email? Valid, reachable phone? Confirmed before your AI ever responds.
Interests
15 categories, benchmarked vs. country average — what the chatbot should actually lead with.
Health Scores
Social, Email, Phone, Fraud, Buyer Power (in dev.) — a snapshot of how real and reachable this customer is.
Predictive Matching
Every visitor matched against the segment your team defines, automatically, in the same call.
Digital Footprint
Where they actually live online — the strongest single tell that separates a person from a fabricated identity.
AI Customer Persona
A ready-made persona your chatbot or support AI can act on immediately — instead of guessing.
Conversation Stage
First contact, abandoned mid-flow, or repeat conversation — know exactly where this person stands.
Local Market Presence
100+ local platforms per country. A malicious bot typically has none — a real shopping agent's principal usually does.
Same 6,000 users.
Same re-engagement budget. Very different outcome.
A worked example, from the AI product's side — what changes when SocialScore sits between your user base and your next campaign.
Why: the 210 vs. 470 reactivations are the same €9,000 campaign budget — the only thing that changed is knowing who's a real, engaged persona worth reaching, from the €6,000 identification pass. That's the growth lever. Separately: fake and bot-created accounts get flagged before your team spends message credits or ad spend chasing them — that's the waste-reduction lever, and it runs every month after this one without new setup.
Same checkout page.
Three very different visitors.
AI shopping agents are already buying — AI-referred traffic grew 805% year over year in late 2025. Most checkouts still can't tell an agent, a real person, and a bad bot apart, so they either block a good buyer or wave through a fraud attempt.
Nine ways this turns into
money you can point to
User Health Report
Branded PDF from a CSV upload — forwarded up the chain, not filed away.
Google Ads & Meta Export
Push your most-engaged persona as a hashed-email audience for acquisition.
Human-or-Bot Engine
Route agents and humans to the checkout each was actually built for.
Precision Targeting
Acquisition spend goes to Predictive-Match visitors, not broad targeting.
Needs Mapping
Know what this visitor actually wants before your chatbot opens its mouth.
Content & Creative Direction
Chatbot tone and offers shift by persona — instead of one script for everyone.
Support Path Personalization
Known high-value or high-urgency users route straight to a human — no false deflection.
Welcome & Onboarding Flow
Day-1 messaging tailored to the persona, not a generic product tour.
Channel & Timing Optimization
Right channel, right moment, based on the digital footprint already on file.
50,000 chatbot conversations/month,
DTC retail brand
Personalized chatbot experiences see a documented 20–35% conversion lift over generic ones — but only when the bot actually knows who it's answering.
Why, and where the data comes from: before the chatbot answers, SocialScore matches the visitor's email or session to their real interests, purchase history, and predicted segment — pulled from public digital-footprint signals, not invented by the model. That's what a hyper-personalized chatbot actually needs: not a better model, but real context to be personal with. It's also what keeps the bot from filling gaps with a confident guess. Cost basis: ~40,000 unique visitors identified/month × €1. Conversion lift: 20–35% documented range for personalized vs. generic chatbot experiences (industry benchmarks, 2026); this case uses the midpoint.
40,000 support tickets/month,
B2C subscription brand
A ticket "deflected" without customer context isn't resolved — industry data shows true resolution runs 15–25% below the raw deflection number, because the customer comes back through another channel.
Why, and where the data comes from: the same profile already built for onboarding and marketing — verified identity, plan tier, engagement history — is reused by the support AI at zero incremental SocialScore cost. Instead of resolving generically, it answers with the customer's actual context, so it escalates the tickets that truly need a human and genuinely resolves the rest. Re-contact benchmark: true resolution runs 15–25% below raw deflection industry-wide (eesel AI, 2026); this case assumes a conservative −12pp improvement, not full elimination. Cost basis: cost per human-handled re-contact ~€12.
18,000 AI-agent checkout sessions/month,
DTC retailer
AI-referred shopping traffic grew 805% year over year in late 2025 — but a checkout built only for humans converts agent sessions far worse than it should, and can't tell a legitimate agent from a bad bot either.
Why, and where the data comes from: the Human-or-Bot signal reads digital footprint, browsing pace, and agent-identity markers to tell a real shopping agent from a malicious bot in the same call already used for verification. Legitimate agents get a structured, machine-readable checkout instead of a CAPTCHA built for humans; malicious bots get blocked before they reach it. AI agents also generate rapid, structured request patterns that legacy fraud systems misflag as suspicious (industry reporting, 2026) — this signal is what tells the two apart. Cost basis: ~13,000 unique sessions checked/month × €1.
The Customer Intelligence Layer
vs. what you have today
| Comparison | SocialScore | Generic LLM / RAG Chatbot | In-House Build | Bot-Blocking Tool |
| Cost to start | €0 | Included, but no customer data | €500K+ | €50K+/yr |
| Knows WHO it's talking to | Yes, 300+ signals | No — grounded on docs, not people | Depends on build | Blocks, doesn't identify |
| Distinguishes agent vs. bad bot | Yes, in the same call | Not its job | Rarely built for it | Often blocks both |
| Time to first result | 1 day | Immediate, no personalization | 6+ months | 2+ months |
| Reduces hallucination risk | Grounds on the real person | Grounds on documents only | Depends on build | Not its job |
What's actually broken about knowing the customer today
Built to
improve what you run, not to replace it
SocialScore was designed as a context layer from day one — your chatbot platform, support stack, and checkout keep working exactly as they do today.
Your own Data Science team, on call
Every account includes access to our data science team — for signal interpretation, custom integration logic, and model grounding.
Customized to your exact stack
Proprietary chatbot or support platform? We build a custom module to your specification — same enrichment, embedded into your flow.
MCP-Native
Your chatbot or agent pulls signals mid-conversation via MCP — the same context a human rep would have.
Any Platform, Any Webhook
Any platform with a webhook or an import can pull SocialScore signals through the API — no vendor lock-in.
Feeding your AI real context means
compliance isn't optional
Because SocialScore only returns informational signals and never makes the personalization, approval, or routing decision itself, it sits outside GDPR Art. 22's automated-decision rules — your chatbot, your support AI, and your checkout logic make every actual decision, using the context we supply.
GDPR — Standard Tier
Public digital-footprint data only — never Art. 9 special-category data.
ISO/IEC 27001:2022
Security practices aligned to the current information-security standard.
DPA Available
Put in place before any production use.
Pass-Through by Default
No PII retained unless you choose Storage Mode.
EU-Based Entity
DSNTech LTD, Sofia, Bulgaria.
Never Resold
Your enriched data is yours, never sold on.
See
your own users
through SocialScore — free.
Bring your own user or visitor list. We'll enrich it live, in front of you, and show you exactly who — or what — is behind those sessions today.
office@socialscore.io · dimitar@socialscore.io · DSNTech LTD, Mladost 2, Sofia, Bulgaria