science Applied AI Research

AI can think.
We make it
know and act.

Models are powerful reasoners with no memory, no knowledge of your business, and no ability to do anything about it. We build the technology that gives them context and executability — deployed as bespoke systems for your team.

Request a Discovery Call See How It Works
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Agent Access
A capable agent working alongside every person on your team
article article article psychology
Your Knowledge
Every document, policy, and decision your organization has made
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Human Approval
Every agent action surfaces as a proposal. You decide before anything changes
person person person person
person person person person
× 2.4 per seat
Multiplied Output
Every seat in your organization produces measurably more
labs Our Research

The model is not the bottleneck.
The wiring is.

Large language models are extraordinary reasoners. But reasoning alone doesn't do anything. A model that can't read your contracts, search your knowledge base, or propose a document edit is just a text box — no matter how intelligent it is.

The gap between "a model that thinks" and "a system that acts" is two unsolved problems: context and executability. That gap is what we research.

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Context

Getting the right information in front of the model at the right time. Retrieval, memory, document state, organizational knowledge. Without context, the model is guessing. With the right context, it's an expert on your business.

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Executability

Giving the model safe, scoped ability to affect the real world. Tools, human-in-the-loop approval, file operations, document editing. Without executability, the model is a conversation. With the right guardrails, it's a collaborator.

Models are commodity — getting cheaper and more capable every quarter. The wiring that makes them contextual and executable inside a real workflow is not. That's what we build.

Context + executability vs. generic chat
Agntic Copilot Claude Gemini
lockData privacy On-premises Cloud Cloud Cloud
constructionBespoke build Custom-built Off-the-shelf DIY Off-the-shelf
paletteBrand identity Your brand Microsoft Anthropic Google
securityData security Zero external exposure Shared infra Shared infra Shared infra
manage_accountsOngoing service Fully managed Self-service Requires eng. Self-service

How It Ships.

Our research ships as bespoke deployments. Discovery maps your context. Build wires the executability. Retainer holds us accountable to it — every quarter, by the numbers.

folder_open Docs & SOPs
→
workspaces Workflows
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query_stats Metrics
Baseline output per seat: established ✓
Phase 01

Discovery

We begin with a structured audit of your team's most valuable workflows. Where does time disappear? Where do people re-find the same information repeatedly? Which outputs could be produced faster with the right answer already surfaced?

The output is a knowledge map and a measured baseline — output per seat, before the agent. That number becomes the benchmark every quarterly retainer review is scored against.

description SOPs
table_chart Data
picture_as_pdf Docs
Vault indexed
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computer Windows
Phase 02

Build & Deploy

Your documents, SOPs, historical decisions, and data are ingested into a searchable vault — indexed for both semantic meaning and exact keyword matching simultaneously. The agent finds the right answer whether someone describes a concept or types a specific policy number or client name.

Delivered as a white-labeled native desktop app under your brand. No browser tab. No SaaS login screen. No third-party name visible to your staff. The app that lives in their dock says your firm.

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Vault
Model
Scoring
Tools
4.25
Q1 Score
Phase 03

Retainer

We manage the system on retainer. As your business changes, the vault changes with it. As better models become available, the agent is upgraded. New tools added, new workflows covered. The system compounds in proportion to how seriously your team uses it.

Every quarter we score the deployment across four dimensions — Adoption, Output, Vault Quality, and Reliability — and review the number with you. We walk into every retainer review with the score before you ask for it.

The agent proposes.
You decide.

AI that acts without permission is a liability, not an asset. Every output Agntic produces is a proposal — reviewed, approved, and owned by a human. That is not a feature. That is the operating principle.

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No autonomous actions. Ever.

The agent does not write, send, submit, or modify anything without an explicit human decision. Every action requires a click. Autonomy without oversight is not intelligence — it is risk.

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Accountability by design.

When something is approved, a person approved it. When something is rejected, a person rejected it. There is a clear line of ownership at every step — one that holds up under audit, compliance review, or a difficult client conversation.

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Trust earned, not assumed.

Trust in AI systems is built one approved proposal at a time. We do not ask your team to trust the agent on day one. We build that trust through consistent, transparent behavior — every output visible, every decision logged.

1 Proposed Change
Agent

Contract Summary · Section 4.2

The review timeline for standard agreements is

approximately 5–7 business days
typically 1–2 business days

when structured document intelligence is applied at intake.

Sourced from: contracts-sla-2024.pdf
database The Vault

Context is everything.
This is where it lives.

A model without context is guessing. The Vault gives it your organization — every document, SOP, case file, and data export — indexed, fused, and retrieved on every turn. This is the context layer that makes AI useful.

Two search engines.
One answer.

Generic AI retrieval uses one path: semantic similarity. That works for concepts. It fails for exact terms — client IDs, policy numbers, contract clauses, product SKUs. Agntic runs both paths simultaneously and fuses them with Reciprocal Rank Fusion. Concept queries and exact-match queries both land correctly, every time.

psychology
Dense vector search

Understands meaning and context. Finds the right document even when your team doesn't use the exact words it contains.

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BM25 keyword search

Finds exact terms. Policy codes, client names, product IDs — when precision matters more than interpretation, keyword search delivers.

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RRF fusion + MMR reranking

Both result sets are ranked and merged mathematically. Duplicate-penalized, diversity-weighted. The best answer floats to the top with no manual tuning.

Live Retrieval · vault_search
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query: "limitation of liability Whitmore"
psychology Dense vector → 8 candidates sim 0.87
search BM25 keyword → 8 candidates exact
↓
RRF fusion · MMR rerank · top 4 returned
✓
Whitmore_Engagement_v4.docx · §8.4 relevance 0.94
Grade: relevant ✓  ·  corrective retry: not needed  ·  sources injected into context
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Live vault. Real-time ingest.

Drop a file into the vault folder and it is indexed within seconds — no manual uploads, no batch jobs. The agent has access to your latest documents the moment they land.

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Every format. Fully readable.

PDF, Word, Excel, PowerPoint, images, and scanned documents — all ingested via OCR and deep document parsing. Your knowledge base does not care what format your files are in.

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Grounded. Not hallucinated.

Every answer is grounded in retrieved source material. If the retrieval grade is low, the agent rewrites the query and retries. It does not guess when it can look it up.

The model is a deployment detail.
The wiring is the product.

Every deployment runs the same context and executability layer. The inference engine underneath is chosen during Discovery based on your team's privacy requirements, budget, and use case.

cloud

Cloud inference

Powered by frontier models from Anthropic or OpenAI. Best reasoning quality, fastest to deploy, scales instantly. Token costs grow with usage.

  • checkFrontier-class reasoning and instruction following
  • checkZero hardware requirements
  • checkFastest path from Discovery to live deployment
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Local inference

Model runs on your hardware via optimized local runtime. Data never leaves your environment. One-time build cost, unlimited queries, zero vendor dependency.

  • checkComplete data privacy — compliant by architecture
  • checkFlat cost regardless of seats or query volume
  • checkNo external API dependency or rate limits

Both paths run the same context layer, the same tools, the same HITL approval flow. The choice is about privacy and economics — not capability.

White-Label Deployments

Employees never
know it's us.

Every deployment ships under the client's name, logo, and color scheme. The app in your team's dock says your firm — not Agntic. This is not cosmetic. It determines whether people open it.

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Custom app name, icon, and color palette matched to your brand identity

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Native macOS and Windows — no browser tab, no SaaS login, no third-party branding visible to staff

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Agent persona scoped to the domain — a legal deployment knows contracts, not logistics

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Each client's vault, model, and interface is fully isolated — no shared infrastructure

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Harlow & Partners
Legal Intelligence
Live
Legal Intelligence

The firm standard is 12 months of fees.

Clause 8.4 in Whitmore falls short at 6 months — a revision has been proposed.

Holloway v. Ashby [2024] supports the 12-month position.

description Firm_Standard_Clauses.pdf
description Whitmore_v3.docx
trending_up
Meridian Capital
Portfolio Intelligence
Live
Portfolio Intelligence

Growth Fund returned 4.2% in March, outperforming S&P 500 by 1.8%.

Top contributors: MSFT (+12.3%), NVDA (+8.6%). Allocation within mandate.

Performance report drafted and ready for client distribution.

table_chart March_Performance.xlsx
description Portfolio_Mandate.pdf
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Crest Property Group
Listings Intelligence
Live
Listings Intelligence

4 properties exceed 60 days: 14 Harborne Rd (72d), 7 Mill Lane (81d).

22 Poplar St (68d) and Flat 3B Kingsway (91d) also flagged for review.

Price adjustment recommended for 7 Mill Lane and Flat 3B Kingsway.

table_chart Thornton_Listings.xlsx
description Pricing_Guidelines.pdf
local_shipping
Vantage Operations
Operations Intelligence
Live
Operations Intelligence

Tier 1 SLA compliance: 94.2% for Q1. Two incidents exceeded the 4-hour window.

Both fell in the Northern region — root cause documented and escalated.

Corrective protocol initiated. Escalation report attached for review.

table_chart SLA_Tracker_Q1.xlsx
description Incident_Log_March.pdf
Powered by Agntic OS

Context + executability = measurable output.

The measure of every deployment is simple: does each seat produce more in less time?
If yes, the wiring is working. If not, we fix it.

Baseline output per seat
+ Agent access
+ Your organization's knowledge
+ Retrieval on every turn
+ Human-approved document drafting
Multiplied output per seat
Deployment health

Context quality is measurable. We measure it.

Faithfulness checks, retrieval scoring, and output gates run on every interaction. If the context layer degrades, we know in hours — not quarters.

Faithfulness 91%
Answer grounded in retrieved vault sources
Retrieval quality 87%
Relevant documents retrieved per query
Output gate pass 96%
Structured outputs validated before delivery
Tool reliability 99%
Tool calls completing without error
93%
Deployment Health
Thriving
7-day rolling avg
across all queries

Three phases.
One continuous engagement.

Every deployment is a research engagement — we learn what context your team needs and what the model should be able to do, then we build and maintain it.

One-time fee

Discovery

Map the workflow. Define use cases. Establish the output baseline your retainer will be scored against.

  • check_circle Workflow Audit
  • check_circle Use Case Definition
  • check_circle Output Baseline Measurement
Book Discovery
Project cost

Custom Build

Design, implement, and deploy the white-labeled desktop agent trained on your knowledge base. Native macOS and Windows.

  • check_circle Native macOS & Windows App
  • check_circle Custom Agent & Brand Identity
  • check_circle Knowledge Vault Injection
Schedule Build
Monthly recurring

Retainer

Vault maintenance. Model upgrades. New tools. Quarterly scoring. Continuous performance management.

  • check_circle Vault Maintenance
  • check_circle Quarterly Performance Scoring
  • check_circle Continuous Tool Library Upgrades
Request Access

Pricing discussed on a per-engagement basis. Every deployment is different in scope and team size.

edit_note From the Lab

Research & Writing

How we think about context, executability, and the systems that connect AI to real work.

View all posts →

Ready to give AI the context
to actually know your business?

Start with a Discovery call. We map what your team needs the model to know, what it needs to be able to do, and what output per seat should look like after 30 days.

Book a Discovery Call