Strategic Intelligence Briefing  ·  Prepared for the Trell Systems leadership teamIndependent research by BioCreative Strategies
BioCreative Strategies
For Tyler Gadoury & the Trell Systems team

A custom commercial system, built for Trell in three months.

A custom React app, your own SQL database, a knowledge graph of every Director+ MS&T, Quality, and Manufacturing buyer in GMP pharma and biotech — multi-channel outreach across LinkedIn and email — and a foundation that scales as Trell moves from first customers to enterprise pipeline.

Everything on this page is built specifically for Trell Systems. The intelligence is live. The infrastructure is ready to deploy.

The full BioCreative engagement

Three waves. One commercial engine. Yours at the end.

The BioCreative Launch program builds Trell's complete commercial system in three overlapping waves — research and knowledge graph first, then the database + UX + sending infrastructure, then live activation across LinkedIn and email. Every artifact is yours to keep.

Wave A — Month 1

Intelligence + knowledge graph

Deep market research, ICP refinement across 6 buyer personas (VP Quality, Director MS&T, Head of Tech Ops, Director Manufacturing, Director CMC, Head of Process Dev), account universe mapping, competitive positioning vs. Veeva/MasterControl/Eigen. Knowledge graph populated with real manufacturing buyer signals.

Wave B — Month 1–2

Database, UX, sending infrastructure

Postgres database seeded with 7,800+ ICP accounts and 39,000+ contacts. Custom React dashboard deployed, sending domains warmed, HeyReach + EmailBison configured on dedicated IP. Built in Trell's brand, behind your team's login.

Wave C — Month 2–3

Activation across LinkedIn + Email

Multi-channel outreach live targeting Director+ decision-makers at GMP pharma and biotech manufacturers. Custom messages generated per persona, sent through warmed infrastructure. Reply classification active. Pipeline reporting live. Meetings booked.

Every section below dives into a piece of this. What we build, how it works, and proof it's already running.

Your app build — what you own

The core stack we build, harden, and hand back.

A complete commercial system — custom React UX, Postgres database, backend automation, and hardened sending infrastructure — built in Trell's brand. Cancel anytime and take everything with you: the app, the database, the server, the code. No lock-in, no black box.

Custom UX

Your React App

Accounts, campaigns, news, outreach — one operator UX in your brand, behind secure login. Hosted on your virtual server. Transferable to any host.

Database

Postgres / Supabase

SQL database. Real-time, exportable, API-accessible. Every table, every row — yours.

Hosting

Virtual Server

The backend hub for everything we build — APIs, automations, news ingestion, channel connections all run here. Available for migration if needed.

Code & IP

GitHub

Every line of code — front-end, back-end, schema, knowledge graphs, data pipelines, agent harnesses, prompts. All in your repo, all yours to transfer.

Backend

n8n + Custom Harnesses

Edge functions, coordinated n8n flows, custom Python and TypeScript harnesses — all executing on your server. The automation layer that keeps everything running.

LinkedIn

HeyReach

Multi-account LinkedIn automation. Your workspace, your rep accounts, your campaign data.

Email

EmailBison

Warmed sending infrastructure on a dedicated BioCreative IP. Your domains, your mailboxes.

Every piece above becomes Trell's property at handoff. The UX, database, server, and GitHub repo form a transferable unit — move it anywhere, run it independently.

Our backend research engine — what powers your database

How we fill your knowledge graph.

These are the tools and feeds we operate continuously to populate Trell's database with classified accounts, enriched contacts, scored news, and live intelligence. You don't license these individually — we run them and the structured output lands in your database, ready to power messages, the live newsfeed, and your team's daily UX. We've already used a combination of these tools to begin building this page and populating yours.

Clinical

ClinicalTrials.gov

Sponsor, site, PI, phase, indication — live trial graph.

Literature

PubMed · bioRxiv

Publication record, co-author graph, preprint signal.

Funding

NIH RePORTER

Active and historical grants, awards by lab and PI.

Regulatory

FDA + SEC + USPTO

Regulatory, capital markets, and IP signals.

Enrichment

Clay

Waterfall enrichment — emails, firmographics, technographics.

Enrichment

FullEnrich

Multi-source contact verification and enrichment cascade.

People

Sales Navigator

Title, tenure, company moves, intent signal.

Web intelligence

Firecrawl + Brave Search

Full-page scrapes of competitor sites, prospect pages, branding, and layout patterns — plus real-time web search for news, market signals, and public intelligence. Feeds knowledge graph continuously.

News feeds

RSS + News APIs

Self-contained pipeline: RSS feed scrapes + news API calls → keyword alignment against your watchlist → AI classification and per-client summaries. Runs 2×/day. Powers the live newsfeed in your UX.

Static LLMs we orchestrate

Claude · GPT · Gemini · Perplexity — these LLMs work as static processors. We engineer the context around them — what goes in determines what comes out.

Custom news intelligence — a self-contained pipeline feeding your knowledge graph

Scored news, in your UX, updated twice daily.

We build a custom news pipeline for each client: RSS feed scrapes + news API calls pull raw articles from dozens of sources, then custom code runs keyword alignment against Trell's watchlist — your competitors, target accounts, therapeutic areas, regulatory bodies. Brave Search adds web-level enrichment where RSS gaps exist. An AI classifier scores each article for relevance and writes a per-client summary of why it matters to your business.

Pharmaceutical Technology · May 2026

FDA Issues Updated Guidance on AI/ML Applications in Pharmaceutical Manufacturing

Regulatory tailwind for Trell — the guidance specifically addresses evidence documentation requirements for AI-assisted manufacturing processes. Companies adopting AI in GMP environments will need validated evidence reconciliation, positioning Trell as the compliance layer that makes AI adoption safe.

Contract Pharma · May 2026

Catalent Expands Digital Manufacturing Capabilities Across 5 Biologics Sites

Key ICP account investing in manufacturing digitization. Their batch record volume across 5 sites creates massive evidence compilation overhead — exactly the pain point Trell solves. Pre-outreach can reference this investment and position evidence reconciliation as the missing compliance layer.

Two real items pulled and scored this week. The system runs continuously — you see every signal that matters, nothing that doesn't.

Proof we've already started populating your knowledge graph

We've already mapped Trell's buyer landscape.

Research covering Trell's competitive positioning, buyer universe, and market dynamics in regulated manufacturing technology. Three highlights below.

Competitive

The QMS market is consolidating — but evidence reconciliation remains unaddressed.

Veeva Vault Quality, MasterControl, and Dot Compliance dominate the QMS space, but none offer AI-driven evidence reconciliation. Trell's positioning as a layer that sits alongside existing EBR/MES/QMS/EDMS systems — rather than replacing them — creates a unique wedge into organizations already locked into enterprise QMS contracts.

Buyer signals

7,508 Director+ decision-makers identified across MS&T, Quality, and Manufacturing.

Querying the BioCreative master database (222K+ contacts across 14,132 life sciences accounts), we identified 7,508 Director+ contacts in Trell's exact buyer personas. 770 CDMO-specific accounts with 825 Director+ targets represent the highest-density starting point.

Market dynamics

FDA's 2025 AI guidance creates tailwinds for evidence automation.

The FDA's evolving guidance on AI/ML in manufacturing (GAMP 5 2nd Edition, Annex 11 updates) is creating urgency around validated evidence documentation. Companies adopting AI in manufacturing need evidence reconciliation that meets GxP standards — exactly Trell's value proposition.

Same knowledge graph, same data layer, same UX you'll log into. We've started; activation is the next step.

From raw data to outreach-ready contacts

Multi-level enrichment. Multi-source find-people. Classified for fit.

Every account and contact in Trell's database runs through a layered enrichment pipeline before a single message gets generated. Each level is a deterministic gate — cost-controlled, source-traced, validated. Junk goes out; signal stays in.

Account track

Multi-level enrichment + ICP scoring

Accounts run through progressive enrichment levels — light enrichment fills missing data via search + LLM, deep enrichment runs category-aware research into structured intelligence fields. ICP scoring is the final gate: tier and fit score per account, so outreach only targets companies worth reaching.

Contact track

Find-people + enrichment cascade

Sales Navigator + PhantomBuster + Clay's multi-provider find-people waterfall + custom scrapes locate the right contacts at every targeted account. Then full-profile enrichment runs: headline, summary, publications, work history, verified email waterfall — everything needed to write a message that reads like a human did the research.

Classification

Marketing persona mapping

Every eligible contact gets a Claude-graded marketing persona classification for the SSO matrix. Persona tags drive message differentiation, channel prioritization, and sequence selection. Trigger detection — job changes, publications, funding events — populated continuously.

By the time the messaging engine runs, every contact already carries enrichment, fit score, and persona classification. The same tags power the SSO matrix and every channel downstream.

How every message gets written

SSO Matrix — a context engineering agent, not a prompt.

When it's time to write an email or LinkedIn message, we don't ask an LLM to “be creative.” We run the SSO Matrix — a context engineering agent that pulls everything relevant about this person, this account, this moment — and assembles a precise context window before the LLM ever sees the task.

Pain points and value props aligned to the ICP and buying persona · enrichment record (trials, publications, funding, news, role context) · recent triggers · message-thread history · brand voice tokens. All assembled, then handed to a static LLM for generation.

1
Pull
Enrichment record + ICP classification + persona + triggers + brand voice tokens
2
Assemble
Context engineered into a structured matrix — the SSO payload
3
Generate
Static LLM writes the best possible message for THIS person at THIS moment
4
Score + ship
Brand Voice Guardian + deliverability checks → send via HeyReach or EmailBison

Following Karpathy's method

The LLM is a static processor. The intelligence comes from the context engineering — what we pull, what we filter, what we hand it. All the work happens before the model sees the task. That's why every message reads like it was written by someone who knows the recipient's business.

Channel 1 — LinkedIn via HeyReach

Blank connection → accept → follow-up DM → reply handoff.

Multi-account LinkedIn automation running across multiple rep profiles. Connection requests, DM sequencing, acceptance tracking, reply classification — all orchestrated through HeyReach. Each rep account warms independently so no single profile gets throttled.

1
Blank connection
Clean request sent from rep's account. No note — higher accept rate.
2
Accept tracked
Webhook fires the moment they accept. Database updated in real-time.
3
Follow-up DM
SSO Matrix generates a message personalized to role, company, and enrichment data.
4
Reply → rep
Reply classified and handed to rep for human close. No AI replies without approval.

Generated examples — different persona, different message

VP Quality
Director MS&T

Dr. Sarah Chen, VP Quality & Compliance at Catalent Biologics

LinkedIn DM (after connection accepted)
Hi Sarah — noticed Catalent's biologics division recently expanded its batch record digitization initiative. We've built an AI layer that generates source-linked evidence packages from existing EBR/MES data — sits alongside Veeva/SAP rather than replacing them. Would love to share what we're seeing in the evidence reconciliation space if relevant to your quality ops.

Michael Torres, Director Manufacturing Science & Technology at Lonza Biologics

LinkedIn DM (after connection accepted)
Michael — saw Lonza's recent tech transfer acceleration announcement for the Vacaville site. We work on AI-driven evidence reconciliation for GMP manufacturing — generating source-linked evidence packages that sit alongside existing MES/QMS. Curious if evidence compilation is a bottleneck in your tech transfer documentation. Happy to share context.

Why blank connections work

Connection requests without notes consistently outperform noted ones in accept rate. The follow-up DM — sent only after they accept — is where the personalization lives. By then they've already signaled intent.

What you own

HeyReach workspace, rep account access, campaign data, all sequence logic. Sales Navigator identifies the right contacts. BioCreative's classification agents ensure each connection hits the right persona at the right tier.

Channel 2 — Email via EmailBison

AI-generated sequences. Warmed inbox. Per-recipient personalization.

Purpose-built email sending infrastructure with dedicated warmed domains. Multi-step sequences, real-time tracking, reply webhooks, bounce handling. Every email lands in inbox because the infrastructure is engineered for it — not bolted on.

1
AI drafts sequence
SSO Matrix generates multi-step emails from knowledge graph + enrichment record. Per-persona, per-account.
2
Warmed inbox send
Delivered via verified sending infrastructure. DNS, DKIM, SPF, warm-up all handled.
3
Tracking
Opens, clicks, replies tracked in real-time. Engagement scoring updates the contact record.
4
Reply → rep
Reply webhook fires. Classified (interested / objection / referral / meeting). Routed to the right rep.

Generated examples — different persona, different message

VP Quality
Director MS&T

Dr. Sarah Chen, VP Quality & Compliance at Catalent Biologics

Email — Step 1
Subject: Evidence reconciliation at Catalent Biologics Dr. Chen, I lead commercial development at Trell Systems. We've built an AI reconciliation layer that generates source-linked evidence packages from existing manufacturing data — designed to sit alongside your current QMS/EBR/MES stack rather than replace it. The output isn't another dashboard. It's work your QA team can approve — every claim linked back to its source record. Given Catalent's scale across biologics manufacturing, I thought it might be worth a conversation about how evidence reconciliation could reduce review cycle time on batch disposition. Would a 15-minute call next week make sense? Best, Tyler
Email — Step 2
Subject: Re: Evidence reconciliation at Catalent Biologics Dr. Chen — following up briefly. One data point that might be relevant: our pilot customers are seeing 60-70% reduction in manual evidence compilation time for batch record review. The system builds operating memory over time — each package makes the next one faster. Happy to share a sample evidence package if helpful — no commitment needed. Best, Tyler

Michael Torres, Director Manufacturing Science & Technology at Lonza Biologics

Email — Step 1
Subject: Evidence compilation in tech transfer workflows Michael, With Lonza's expanding tech transfer volume across sites, I wanted to introduce Trell Systems. We've built an AI reconciliation engine specifically for GMP manufacturing evidence. Send in your batch records and process data — get back a source-linked evidence package your team can review and approve. Every claim traced to its origin record. The system is designed as a layer alongside your existing EBR/MES/EDMS — not a replacement. It builds operating memory over time, so each evidence package gets faster and more accurate. Worth a quick conversation about how this might fit into MS&T workflows at Lonza? Best, Tyler
Email — Step 2
Subject: Re: Evidence compilation in tech transfer workflows Michael — brief follow-up. Wanted to flag: we're seeing particular traction with MS&T teams managing multi-site tech transfers, where evidence compilation across different source systems (different MES versions, legacy batch records, etc.) creates the biggest bottleneck. If that resonates with what you're seeing at Lonza, happy to show you a 5-minute demo of the reconciliation engine in action. Best, Tyler

Why we build separate sending domains

Protect your core domain

Trell's primary domain reputation stays untouched. Outreach runs on dedicated domains we build, verify, and warm up over 2-4 weeks before a single cold email sends.

Full DNS + authentication

SPF, DKIM, DMARC, custom tracking domains — all configured from day one. Deliverability monitoring runs continuously. If a domain cools, we rotate.

Land in inbox, not spam

Gradual warm-up cadence, engagement-based send scheduling, bounce suppression, spam trap monitoring. Best practices baked into the infrastructure from day one.

Dedicated BioCreative sending IP

Why off-the-shelf senders can't match this.

Off-the-shelf senders (SmartLead, Lemlist, Instantly) route through shared IPs sending for hundreds of accounts. Microsoft and Google are increasingly throttling or blocking these IPs at the inbox boundary — placement collapses regardless of your domain reputation.

We send from a dedicated IP we own, used only for BioCreative clients, kept clean by tight quality control across every domain, mailbox, and message. Low-volume, warmed sends only. No links in cold messages. Every send vetted. That's why our deliverability holds where others' don't.

What you own: Sending domains, mailbox seats, all campaign data, sequence logic, reply history.

Two channels. Coordinated. One system.

LinkedIn + Email running off the same enrichment data, same classification, same knowledge graph. Multiple rep profiles per channel. One operator dashboard. Every touch coordinated so a prospect never gets conflicting signals — just consistent, intelligence-grounded outreach from Trell's team.

How it works

Wave A → Wave B → Wave C. Three months to a running engine.

Each month maps to a wave. Intelligence first, then infrastructure, then activation. Every artifact yours to keep.

Month 1Wave A — Intelligence

Wave A — Intelligence + knowledge graph

Deep research dossiers, ICP refinement across 6 buyer personas, account universe mapping (7,800+ GMP manufacturers), competitive positioning, news ingestion. Database schema designed and seeded with 39,000+ contacts.

Delivered: Research dossier set, enriched account universe, contact records with email + LinkedIn coverage, ICP schema, knowledge graph live, news scraping + scoring active.

You keep: All data, schema, query layer, refresh runbooks.

Month 2Wave B — Infrastructure

Wave B — Database + UX + sending infrastructure

Custom React app deployed on provisioned server. Account pages, campaign tooling, newsfeed, message drafting all live behind your login. Sending domains warmed; HeyReach + EmailBison hardened on dedicated IP.

Delivered: Custom React UX behind login, virtual server provisioned, GitHub repo populated, warmed sending domains, HeyReach workspace, EmailBison configured, dedicated IP active.

You keep: Hosting, code, sending domains, mailbox ownership, all workflow configurations.

Month 3Wave C — Activation

Wave C — Activation across LinkedIn + Email

Multi-channel outreach live targeting Director+ at GMP pharma and biotech. Custom messages generated per persona against the knowledge graph. Reply classification routing leads to Tyler. Pipeline reporting live.

Delivered: Live LinkedIn + email campaigns, AI-drafted sequences per persona, reply handoff workflow, pipeline dashboard updated, first meetings booked.

You keep: Campaign data, message history, agent prompts, sequence logic, reply records.

OngoingOperate

Optional: continuous tuning + extension into upstream marketing or downstream sales pipeline

Quarterly tune-ups, pre-event intelligence sprints (ISPE, PDA, INTERPHEX), database refreshes, and the option to layer on upstream marketing amplification or downstream strategic-selling pipeline on top of the core system.

Two support systems on top of your core launch

One going upstream into marketing. One going downstream into sales.

Both build on the same database, the same knowledge graph, the same brand system the core launch delivers. Both are scoped separately, activated when ready — not part of the core engagement, but built to plug straight into it.

Upstream — marketing channels that feed your funnel

Top-of-funnel amplification across every surface your buyers see.

Once the core engine is running, the same knowledge graph + brand system + database powers everything upstream — company social presence, executive thought leadership, search and AI-citation visibility, paid amplification, web optimization, and live calling. Marketing-owned, harder to track, but the connective tissue that makes outbound feel like a known voice.

We've already scraped Trell's brand — colors, fonts, layout patterns — directly from trellsystems.com. This page is the proof. Every asset we produce downstream matches your visual identity without manual design work.

Company social presence

Content Command Center

LinkedIn company, X, Instagram, Threads, Bluesky, blog, newsletter. AI-generated social assets with image and video generation. News-driven concept generation matched back to Trell's core thought principles. Brand Voice Guardian scores every output before it ships.

Tracked end-to-end: engagement on company posts feeds back into the knowledge graph as triggers and targeting signals.

Executive thought leadership

Scientific communications

Founder, scientific leader, and executive voice across LinkedIn personal, conferences, partnership outreach, investor updates, analyst notes. Same knowledge graph, same brand system — but a different voice and surface.

CCC connection: Engagement on personal posts is tracked at both the person level and the account level — engaged followers who match ICP become outreach targets automatically.

Search presence

SEO + GRO optimization

Google Analytics 4 + Search Console + AI citation tracking. Every asset keyworded and position-tracked. Trell shows up in traditional search and in AI-generated answers (Generative Research Optimization).

CCC connection: blog posts, social assets, and backlinks from the Content Command Center compound into your search and AI citation presence.

Web presence

Website optimization

Using the same intelligence, brand system, and tools that built this page, we help Trell create comprehensive, interactive, on-brand HTML assets — landing pages, product pages, microsites — informed by market data and kept up to date through our tooling.

Paid amplification

Paid channels

LinkedIn Ads, Google Ads, Meta Ads, programmatic display, retargeting — all powered by the same ICP classification and enrichment data. Target the exact accounts your outreach is hitting with awareness ads that reinforce the message across every surface.

Outbound calling

Cold calling — in partnership with Science2Sales

Dedicated calling team executing to high-value prospects identified by your enrichment pipeline. Direct conversations with people in real labs, real offices, real procurement teams. Market research, voice-of-customer intelligence, and live prospect feedback flow back into your knowledge graph alongside pipeline opportunities.

All built on the same knowledge graph, brand system, and database the core engagement delivers. Engaged after Wave C.

Downstream — strategic selling on the same back-end

Downstream support — strategic selling, powered by the same knowledge graph.

Once meetings start landing, the same back-end becomes the engine for managing pipeline, qualifying opportunities, and supporting Tyler in every meeting. Built on Miller Heiman strategic selling — the methodology that works in complex enterprise sales, now deployable through your custom system.

In core launch

Gold Sheets — account intelligence pages

The account-level page in your UX is the Gold Sheet: full enrichment, news, contacts (scored + persona-tagged), positioning notes, campaign history across LinkedIn and email — evergreen, updated as the world moves.

Downstream add-on

Pipeline tab — opportunities + multi-buyer mapping

When a Gold Sheet matures into a real opportunity (timeline, pricing, qualification), it becomes a pipeline record. Map every buying-decision contact (economic / user / technical / coach), track interactions, and ingest meeting transcripts so the picture stays live.

Downstream add-on

Blue Sheets — meeting prep + in-meeting assets

Before every key meeting, the system generates a Blue Sheet: who's in the room, their place in the buying decision, full prior-engagement history, mapped pain points, prepared objection responses, goals for the meeting, next-step recommendations.

Like the marketing channels above, these are downstream additions to the core launch — not separate builds. The infrastructure is already there; we activate it as your sales motion matures.

You own the system. Period.

Code, data, prompts, dashboards, infrastructure — all transferred to Trell at handoff. We don't run an “AI black box” you keep paying us to operate. What we hand back is yours, the same way Trell hands customers a real platform they own outcomes on.

Next step

Ready to see the full system.

Everything on this page is built specifically for Trell. The intelligence is already underway. The infrastructure is ready to deploy. One conversation to scope it.

— Brian Elbert, BioCreative Strategies
brian@biocreativestrategies.com