Tekmeriα
§ I · The Platform

Meet Your Network of Specialized Predictive Models.

Tekmeria is not a generalist AI. It is a proprietary System of Intelligence designed to replicate the cognitive processes of Health Technology Assessment (HTA) bodies.

While others focus on summarizing public data, we simulate decisions. We have built a “Hive Mind” of country-specific agents—hard-coded with the intuition of industry experts and trained on decades of payer precedents.

§ II · The Architecture

A highly specialized predictive system trained on a comprehensive set of relevant data

We treat HTA strategy as a high-precision data science problem. Our technology stack moves beyond simple text processing to a rigorous, three-layer pipeline that turns regulatory noise into computable, predictive signals.

01
01.

The Data Refinery

Vertical ETL

Turning Unstructured Noise into a Unified Intelligence Grid.

Raw regulatory data is unstructured and disconnected. A 2024 guideline update might invalidate a 2021 pivotal trial design, but standard search tools won’t catch the link.

We have built a Dynamic Intelligence Grid that ingests, cleans, and structures data from disparate sources, creating a unified “single source of truth.”

Semantic Evolution Tracking

Our system acts as a “Time Machine,” tracking how medical concepts (e.g., “Standard of Care”) evolve over decades. We don’t just know what the rules are today; we know how they change, allowing us to predict future requirements.

Fig. ii.i

“Standard of Care” t → 2008 2015 2021 2026

Value Insight

By unifying fragmented regulatory history into a single source of truth, we ensure your strategy is built on the complete picture, not just the visible surface.

i.  INGEST → CLEAN → STRUCTURE → UNIFY Δ(t) semantic drift tracked

02
02.

Granular Feature Engineering

Quantifying Intuition

Quantifying Regulatory Intuition.

The core challenge of HTA is that guidelines are qualitative (text), but prediction requires quantitative data. We bridge this gap by translating regulatory language into math.

Translating Text to Features

We translate qualitative regulatory text into thousands of computable features.

Proprietary Vectorization

Instead of feeding raw text to a model, we engineer proprietary vectors that quantify strategic concepts.

Digitizing Strategy

We turn “regulatory intuition” into math.

Fig. ii.ii

RAW REGULATORY TEXT f: text → ℝⁿ proprietary vector f₂ f₁ (0.71, 0.83)

Value Insight

This digitization of intuition allows us to stress-test your asset against thousands of historical precedents, revealing risks that human review alone would miss.

ii. TEXT → FEATURE → VECTOR → PRECEDENT f: text → ℝⁿ

03
03.

The Micro-Agent Network & Integration

Reconstructing Logic

Reconstructing the Decision Logic.

We move beyond simple prediction to a complete reconstruction of the assessment process.

Specialized Reconstruction

Each model is country-specific and is task-built to reconstruct exactly how HTA bodies make decisions.

Holistic Synthesis

By merging the outputs of these independent, high-precision models, we construct a comprehensive, multi-dimensional view of the HTA outcome.

Mathematical Logic

We don’t just “guess” the result; we mathematically reconstruct the decision logic used by the committee.

Fig. ii.iii

COUNTRY MICRO-AGENTS G-BA HAS NICE EMA FDA 0.80 0.72 0.68 0.75 0.82 w₁ 0.22 w₂ 0.19 w₃ 0.18 w₄ 0.20 w₅ 0.21 synthesis FORECAST 0.94

Value Insight

The result is a forecast that mirrors the actual committee debate, giving you the foresight to preempt objections and secure your target price.

iii. COUNTRY MODELS → SYNTHESIS → FORECAST Σ specialist agents

§ III · The Scale of Intelligence

Evidence-Based. Data-Backed. Battle-Tested.

Our models are not hallucinating; they are referencing a massive, structured dataset of historical reality.

i.

20+

Years of Clinical Guidelines

tracked for semantic evolution

Temporal depth

ii.

15,000+

HTA Decisions

(G-BA, HAS, NICE) ingested and labeled

Regulatory breadth

iii.

120,000+

Clinical Trials

analyzed for comparator and endpoint trends

Clinical corpus

iv.

2,500+

Proprietary Features

engineered per therapeutic indication

Engineering depth

v.

94%

Prediction Accuracy

verified against historical payer decisions

Validation result

Sources: G-BAHASNICEICERPBACCADTHTLVAIFA

§ IV · The “White Box” Standard

Explainability as a Feature.

In the pharmaceutical industry, a "Black Box" prediction is a liability. You cannot base a billion-dollar decision on “The AI said so.”

Traceable Logic via RAG (Retrieval-Augmented Generation)

We adhere to a strict "White Box" architecture. Every prediction, risk flag, or recommendation is citations-backed.

Insufficient — “High risk of rejection.”
↓

Fig. iv. · RAG-Traceable

78% Risk of Rejection

78% Risk of Rejection because the comparator violates G-BA Guideline Section 4 (2023)1 and mirrors the failure pattern of Asset X (2022)2.

1 G-BA Guideline §4 (2023)

2 Asset X HTA dossier (2022)

This is not just data processing. It is the digitization of strategy.

§ V · The New Standard

The Era of Trial and Error is Over.

We have turned the art of Market Access into a solvable science. By replacing subjective guesswork with adversarial simulation, Tekmeria ensures that when you finally face the payer, the outcome is not a gamble—it is a calculated certainty.

Welcome to the new physics of decision-making.

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