01Capabilities / Research + engineering

Technical depth across the AI system—not just the model call.

EAVAE Labs works across agents, retrieval, generative models, evaluation, and focused research prototypes. The scope begins with the uncertain behavior or engineering decision, then selects the smallest useful investigation, prototype, or reliability surface.

Explore the capability map Inspect the research method

Representative system map · synthetic · not a client result

02Capability map

Five connected technical domains.

These are not isolated menu items. Agent behavior depends on context and tools; generative systems need evaluation; research prototypes need an engineering boundary; and reliability evidence should feed the next implementation decision.

01Capability pillarAgentic systemsDesign and evaluate tool-using, stateful workflows where planning, memory, coordination, permissions, and human escalation determine whether useful behavior survives beyond a demo.02Capability pillarRetrieval and knowledge systemsEngineer the path from heterogeneous knowledge sources to useful context, with explicit attention to coverage, freshness, ranking, attribution, and multi-step retrieval behavior.03Capability pillarGenerative and multimodal systemsPrototype and evaluate generative workflows where conditioning, controllability, consistency, human review, and product integration matter as much as a single model output.04Capability pillarEvaluation and reliabilityTurn traces, tasks, incidents, and model changes into an evaluation surface that explains behavior and supports a concrete release or continuation decision.05Capability pillarApplied AI research and prototypingInvestigate whether a technical approach is credible before a larger build, then make the path from result to production boundary explicit.

03Problems we work on

Bring us the part that is technically uncertain.

A strong starting point is a system, experiment, or recurring behavior with a real consequence—not a generic request for an AI strategy deck or an unspecified chatbot build.

01Agent behaviorBehavior does not survive complexityA system works on simple examples but fails across long-horizon, tool-heavy, multimodal, or stateful workflows.

02Knowledge systemsPlausible context still produces weak answersRetrieval appears relevant while freshness, coverage, ranking, attribution, or multi-step search fails at a deeper boundary.

03Generative systemsA generative pipeline lacks controlThe workflow needs better consistency, conditioning, evaluation, or integration into a repeatable product process.

04EvaluationBenchmarks do not explain the decisionScores exist, but the team cannot tell which change matters or how offline evidence maps to actual system behavior.

05Research transitionA promising result has no engineering pathA paper, experiment, or internal prototype appears useful, but feasibility, interfaces, operational limits, and production evidence remain unclear.

06System improvementEvidence is not connected into an improvement loopTraces, feedback, incidents, and eval results exist, but they do not reliably shape the next dataset, prototype, test, or release gate.

04Technical scope

Scope follows the system boundary.

Each engagement selects only the capabilities required by the question. Final inputs, access, evaluation coverage, deliverables, and acceptance criteria are agreed before kickoff.

01 / Capability

Agentic systems

Design and evaluate tool-using, stateful workflows where planning, memory, coordination, permissions, and human escalation determine whether useful behavior survives beyond a demo.

Agent architecture, tool contracts, planning, and task decomposition
State, memory, context management, and long-horizon behavior
Multi-agent coordination and handoff boundaries
Human escalation, observability, and workflow evaluation
Computer-use and action systems with explicit permission boundaries

The work does not assume unrestricted autonomous deployment is appropriate.

02 / Capability

Retrieval and knowledge systems

Engineer the path from heterogeneous knowledge sources to useful context, with explicit attention to coverage, freshness, ranking, attribution, and multi-step retrieval behavior.

RAG architecture, hybrid retrieval, reranking, and query transformation
Agentic retrieval and multi-step search
Context construction, memory, and knowledge pipelines
Citation, attribution, freshness, and temporal retrieval
Retrieval benchmarks, search-quality analysis, and error taxonomies

Evaluation separates retrieval, context assembly, generation, and product-policy failures.

03 / Capability

Generative and multimodal systems

Prototype and evaluate generative workflows where conditioning, controllability, consistency, human review, and product integration matter as much as a single model output.

Diffusion-based and image-generation workflows
Multimodal model and pipeline integration
Prompt, conditioning, control, and consistency experiments
Generative evaluation, dataset slices, and synthetic-data workflows
Human review interfaces and production integration boundaries

This does not claim frontier-scale foundation-model or diffusion-model training.

04 / Capability

Evaluation and reliability

Turn traces, tasks, incidents, and model changes into an evaluation surface that explains behavior and supports a concrete release or continuation decision.

Evaluation design, failure taxonomies, and replay suites
Regression testing, release gates, and red-team scenarios
Online, offline, and human evaluation
Trace analysis, fallback design, and operational boundaries
Cost, latency, quality, and ship, revise, or stop trade-offs

Reliability work reduces uncertainty; it does not guarantee error-free future behavior.

05 / Capability

Applied AI research and prototyping

Investigate whether a technical approach is credible before a larger build, then make the path from result to production boundary explicit.

Research-question definition and literature or approach review
Experimental design, baselines, comparisons, and ablations
Focused prototypes and technical feasibility investigations
Research reproduction where practical
Research-to-production planning and engineering handoff

Research is tied to an experiment, implementation, evaluation, or engineering decision.

09Safe first step

Describe the system behavior your team needs to understand or change.

Start with the technical area, current research or production stage, what is uncertain, and what the team must learn, build, evaluate, or decide. Use sanitized context only.

Discuss a technical problem Inspect the handoff

No credentials, production data, customer records, or private repository access in the first brief.

Prefer to talk it through? Book a 30-minute call