LLM model evaluation for Production AI
Measure accuracy, safety, and reliability before you ship

Abaka runs end-to-end LLM model evaluation—objective benchmarks, model-as-judge, and human evaluation—so your team can validate releases, reduce incident risk, and compare models with confidence across text and multimodal workflows.

Talk to an Expert

If your LLM is evaluated with a handful of ad-hoc prompts, you’re likely under-measuring real production behavior. A single regression in factuality or tool-calling can turn into weeks of triage: support tickets, hotfixes, rollback meetings, and new guardrails that slow roadmap velocity. Teams commonly lose 2–6 weeks per quarter re-litigating “Is this model actually better?” because the evaluation harness isn’t stable, versioned, or representative of user traffic.

The cost of inaction is measurable. When evaluation is inconsistent, you can ship models that are accurate in demos but unreliable in the long tail—especially under multilingual, adversarial, or multi-turn conditions. That increases the probability of harmful or non-compliant outputs, and it forces expensive rework: prompt refactors, RAG tuning, policy rewrites, and patchy rule-based filters. A disciplined LLM model evaluation program turns uncertainty into a release gate with clear pass/fail thresholds and traceable evidence.

The LLM Model Evaluation Bottleneck in AI Development

Quality Decay

LLMs drift—your prompts change, your RAG corpus updates, your tools evolve, and user behavior shifts. Without a maintained evaluation suite, teams confuse short-term “prompt wins” with durable capability gains. Small regressions compound: a 2–5% drop in answer accuracy can look minor in isolation, but it can meaningfully increase escalations and manual review burden across thousands of sessions. Abaka helps you build versioned test sets, rubrics, and scoring that keep quality measurable across releases.

Volume Walls

Evaluation volume grows faster than headcount. Once you test multiple models, multiple prompts, multiple locales, and multiple tool flows, you quickly reach tens of thousands of judgments per release. If each judgment takes even 2–4 minutes to read context, verify citations, and score, you can burn hundreds of reviewer hours per iteration—slowing shipping cadence. Abaka scales evaluation throughput using Abaka Forge workflows, large-model automation, and trained human reviewers so you can run broad sweeps without stalling engineering.

Compliance Friction

Safety, bias, and policy compliance can’t be validated with only automated checks. You need documented rubrics, consistent adjudication, and defensible audit trails—especially when your LLM touches finance, healthcare workflows, or security-sensitive operations. Abaka operates with SOC 2, ISO 27001, GDPR, and CCPA-aligned processes, strict NDAs, and segregated secure pipelines. Your evaluation artifacts—prompts, outputs, labels, and rationales—stay governed and traceable from dataset build to final report.

Objective Benchmark & Test Suite Design

We design evaluation suites that match your product reality—not generic leaderboards. Abaka works with your team to define task families (Q&A, RAG, summarization, classification, tool calling, code generation, policy refusal, multimodal reasoning) and builds test sets with clear rubrics and gold answers when applicable. You can ingest cases from production logs (sanitized), internal SMEs, and curated edge-case libraries. Deliverables include versioned JSONL/CSV test sets, prompt templates, scoring rubrics, and a release-ready harness that supports repeated runs over time.

Human Evaluation with Scholar-Grade Reviewers

When correctness is nuanced—legal reasoning, medical explanations, math proofs, or domain-specific policy—human judgment remains the most reliable signal. Abaka provides vertically specialized annotators and scholar-network reviewers across domains like coding, mathematics, medicine, law, business, and languages. We run multi-layer QA, calibration rounds, and adjudication to keep scoring consistent. Your team gets labeled outcomes (pass/fail, Likert, pairwise preference) plus structured rationales that directly translate into model fixes.

Model-as-Judge Pipelines (with Human Calibration)

For large-scale sweeps, we stand up model-as-judge flows that are calibrated against human-labeled anchor sets. This allows you to evaluate more combinations—prompt variants, temperature settings, tool routing, retrieval depth—without linear growth in cost. Abaka Forge orchestrates judge prompts, structured scoring, and disagreement analysis so you can understand where the judge is reliable and where human review is required. Outputs include judge score distributions, calibration curves, and human-verified subsets for auditability.

Red-Teaming, Safety, and Bias Audits

Abaka runs adversarial evaluation aligned to real misuse patterns: jailbreak attempts, prompt injection against tool/RAG systems, disallowed content generation, privacy leakage probes, and bias testing across protected characteristics. We apply consistent rubrics and severity tagging so findings are actionable. You receive categorized failure cases, reproduction steps, and recommended mitigations (prompt hardening, policy updates, tool permissioning, retrieval filtering, or post-processing). This is especially valuable for agentic systems where tool execution can amplify harm.

Tool & Function-Calling Evaluation for Agents

If your LLM uses function calling (e.g., JSON schema tools, SQL generators, API actions), you need more than “answer quality.” We evaluate planning correctness, tool selection, argument validity, error handling, and safe execution boundaries. Abaka can simulate tool outputs, inject malformed tool responses, and test multi-step workflows to measure robustness. Deliverables include structured traces (messages, tool calls, results), pass/fail criteria per step, and defect taxonomies your engineers can use to improve agents iteratively.

Multimodal Evaluation (Text + Image + Video)

Multimodal models fail in different ways: hallucinating objects, missing fine details, misreading charts, or incorrectly grounding in frames. Abaka evaluates VQA, image captioning quality, chart/table interpretation, interleaved image reasoning, and video spatial reasoning with human rubrics and paired comparisons. We deliver labeled sets and score reports that separate perception errors from reasoning errors—so you know whether to adjust data, model, or prompting. Outputs can be delivered as JSONL with image/video references, rubrics, and reviewer notes.

6-Dimension Scorecards & Release Gates

Abaka’s evaluation is organized under a 6-dimension framework: Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability. This structure helps stakeholders align on what “better” means for your product. You receive scorecards, breakouts by slice (locale, domain, user segment, prompt family), and clear go/no-go thresholds. The result is a repeatable release gate that supports confident shipping—not endless debate.

Abaka Forge Workflows for Repeatable Evaluation Ops

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D. For evaluation, Forge manages task routing, reviewer calibration, QA sampling, adjudication, and exports. We can integrate with your existing stack (e.g., data warehouses, experiment trackers, CI pipelines) via structured exports and versioning. You get a durable evaluation operation that scales with your roadmap, supported by human intelligence and automation.

Why Outsource LLM Model Evaluation

Faster Delivery

Outsourcing evaluation removes the bottleneck of recruiting, training, and managing reviewers—especially for specialized domains and multilingual coverage. Abaka can stand up an evaluation program quickly: rubric definition, calibration, and the first scored run in days rather than months. Your engineers keep building product features while Abaka runs the operational loop—batching, QA, adjudication, reporting—so you can compare models, iterate prompts, and ship releases on a predictable cadence.

Direct Savings

Evaluation costs aren’t only reviewer hours—they include internal coordination, inconsistent scoring, and rework when results can’t be trusted. Abaka centralizes workflows, reduces repeat setup, and provides cost controls through sampling strategies, judge automation, and calibrated human review where it matters. You get clear unit economics for evaluation tasks (e.g., red team cases, defensive coding checks, math capability scoring) and avoid building an internal ops function that distracts from core product.

Risk Reduction

Production LLM failures are expensive: brand damage, policy violations, security incidents, and customer churn. Abaka reduces risk by running structured safety and bias audits, adversarial testing, and tool-calling robustness checks—then documenting evidence in a way stakeholders can trust. We operate with SOC 2 and ISO 27001-aligned controls, strict NDAs, and segregated secure pipelines, giving your team a governed evaluation process suitable for enterprise and regulated environments.

Elastic Scalability

Model iteration cycles can spike evaluation demand overnight: new model releases, retrieval upgrades, agent workflow changes, or multilingual launches. Abaka scales evaluation capacity up or down without forcing your team to over-hire. With a global reviewer base and standardized operations, you can run broad sweeps (thousands of cases) before major launches, then maintain smaller continuous monitoring sets weekly. Elastic scale keeps evaluation aligned to real engineering velocity.

Domain Expertise

Generic reviewers can’t reliably score domain correctness in math, coding, medicine, law, finance, or specialized scientific content. Abaka’s scholar-network domains and vertically specialized evaluators provide the nuance needed for high-stakes judgments. That means better rubrics, better adjudication, and more actionable feedback—like pinpointing whether an error stems from reasoning, missing context, incorrect tool usage, or unsafe policy behavior—so fixes are targeted and faster.

Innovation Velocity

Evaluation shouldn’t be a static checklist. Abaka helps you evolve your program: introducing pairwise preference tests, adding new slices (regions, user personas), extending to multimodal tasks, or shifting from one-off studies to continuous evaluation. With Abaka Forge and a repeatable process, you can experiment with new metrics and methodologies while preserving comparability across time. This accelerates learning and helps your team make confident product decisions backed by measurable evidence.

Industries We Serve

Automotive

Automotive AI increasingly depends on language interfaces: driver support agents, technician copilots, service scheduling, and in-vehicle knowledge assistants. Abaka evaluates factuality and safety for automotive guidance, plus multimodal reasoning when assistants interpret dashboards, manuals, or inspection images. For autonomy programs, we can evaluate agent behaviors and tool-calling workflows that interact with planning stacks, simulation outputs, or diagnostics APIs. You get structured evidence that aligns model behavior with safety expectations and user experience requirements.

GenAI / Foundation Models

Foundation model teams need evaluation that goes beyond leaderboards—especially when shipping to enterprises with strict requirements. Abaka designs benchmarks for reasoning, instruction following, creative writing, multilingual performance, and safety. We can combine objective benchmarks, model-as-judge sweeps, and calibrated human evaluation to compare checkpoints, fine-tunes, and prompt stacks. The result is a repeatable scorecard that helps you prioritize training runs, data strategies, and alignment improvements with confidence.

Embodied AI / Robotics

Embodied systems require evaluation of planning, robustness, and interaction—not just text quality. Abaka evaluates task decomposition, tool use, recovery behaviors, and instruction compliance across multi-step workflows. For multimodal systems, we assess grounding to images/video streams and the ability to follow spatial constraints. We can also structure evaluation around RL environment tasks and agent trajectories, producing labeled traces and failure taxonomies that accelerate iteration on policies, planners, and safety constraints.

Healthcare

Healthcare workflows require careful evaluation of factuality, uncertainty handling, and safe behavior—without making unsupported claims. Abaka evaluates medical knowledge assistants, triage chat flows, summarization of clinical-style notes (de-identified), and patient education content. We implement strict rubrics that prioritize clarity, caution, and escalation guidance. Your team receives scored outputs and categorized failure cases (hallucination, contraindication risk, missing disclaimers) to improve prompts, retrieval, and safety policies.

Retail

Retail copilots and customer support agents must be accurate about policies, inventory, returns, and promotions—and consistent across channels. Abaka evaluates conversation quality, brand tone, and correctness under multi-turn constraints, including tool calls to search catalogs, order history, and knowledge bases. We test long-tail cases (edge return rules, bundle promotions, localization) and provide slice-based reporting so you can improve conversion and reduce support escalations with measurable changes.

Finance

Financial assistants and analyst copilots must be evaluated for safety, bias, and factuality—especially when summarizing filings, generating customer communications, or supporting internal operations. Abaka runs evaluations that emphasize grounded answers, refusal behavior for disallowed requests, and robust tool calling (e.g., retrieval, calculators, workflow APIs). We deliver audit-friendly rubrics and scorecards suitable for risk stakeholders, helping you ship improvements while maintaining governance and traceability.

Geospatial

Geospatial products increasingly use LLMs for map search, location intelligence, incident summarization, and multimodal understanding of imagery. Abaka evaluates grounding, spatial reasoning, and factuality when models interpret coordinates, regions, and time-based events. We can include image/video understanding tasks (e.g., satellite imagery descriptions) and structured tool use (querying layers, filters, or GIS APIs). Outputs include labeled cases and reports segmented by geography and data availability.

Security / Defense

Security-sensitive environments require rigorous evaluation of data handling, refusal behavior, adversarial resilience, and tool execution safety. Abaka runs red-teaming, prompt injection testing against RAG/tool stacks, and robustness evaluation under hostile inputs. We structure findings by severity and reproducibility so your team can harden the system quickly. With secure pipelines and strict NDAs, Abaka supports evaluation efforts that need stronger operational discipline and controlled access.

Agriculture / Industrial

Industrial copilots must operate reliably in noisy, real-world conditions: maintenance logs, SOPs, equipment manuals, and sensor-driven workflows. Abaka evaluates extraction, summarization, troubleshooting guidance, and tool-calling flows that interface with CMMS/ERP systems. We test long-tail edge cases—ambiguous part numbers, conflicting procedures, multilingual field notes—and deliver actionable failure categories. This helps your team reduce downtime risks and improve operator trust with measurable evaluation gates.

How It Works

1) Day 0–3 — Scope, risks, and evaluation blueprint

We start with a short working session to define what “good” means for your LLM in production: target users, top workflows, unacceptable failures, and release cadence. Together we map capabilities to Abaka’s 6-dimension framework (accuracy, robustness, efficiency, safety/bias, tool calling, UX). We also define slices: locales, domains, user personas, and traffic tiers. Deliverables by Day 3 include a written evaluation blueprint, labeling rubrics, scoring scales (Likert, pairwise, pass/fail), and a data ingestion plan.

2) Week 1–2 — Build the test set and rubrics (with calibration)

Abaka assembles or ingests evaluation cases: curated prompts, production-like conversations (sanitized), policy tests, and adversarial probes. We create gold answers where feasible, and define structured rubrics for subjective tasks (helpfulness, harmlessness, tone, completeness, groundedness). Reviewers run calibration rounds to align scoring—then we lock the rubric version for comparability. Outputs include versioned datasets (JSONL/CSV), reviewer guidelines, QA sampling rules, and an initial “anchor set” for model-as-judge calibration.

3) Week 2–3 — Execute evaluation runs and generate scorecards

We run the evaluation across your chosen models, prompts, and configurations. Depending on needs, we combine objective benchmarks, model-as-judge sweeps, and human evaluation with adjudication. Abaka Forge manages workflows, reviewer routing, QA sampling, and exports. You receive scorecards aligned to the 6-dimension framework, slice breakdowns (by locale/domain/prompt family), and a prioritized defect list. We also provide a replayable artifact set so your team can rerun the same suite on future releases.

4) Ongoing — Continuous monitoring and regression gates

After the first program is live, we help you operationalize it: weekly or per-release regression gates, alerting thresholds, and a rolling set of hard cases. As your product evolves, we expand coverage to new tools, new domains, or multimodal inputs without losing comparability. We also maintain a curated red-team library and prompt-injection checks for tool/RAG stacks. The ongoing result is a stable evaluation backbone that keeps your roadmap moving while lowering incident risk.

5) Weekly — Readouts, root-cause taxonomy, and iteration loops

Each week (or each release), Abaka delivers a concise readout: what improved, what regressed, and why. We categorize failures into a root-cause taxonomy—retrieval gaps, reasoning errors, formatting/tool schema issues, policy violations, or judge disagreement. Your team gets actionable examples with context and recommended mitigations. This turns evaluation into a learning loop: adjust prompts, RAG, tool constraints, or data strategy; re-run the suite; and track improvements on the same scorecard over time.

Modality & Format Coverage

LLM model evaluation isn’t limited to plain text. Modern systems combine chat, retrieval, tools, code execution, and multimodal inputs. Abaka supports evaluation across modalities with structured rubrics, calibrated human reviewers, and repeatable exports—so your team can compare models and configurations apples-to-apples, track regressions, and build defensible release gates. Below are common modalities we evaluate and the formats we deliver to plug into your pipelines.

Modality	Annotation Types	Tools	Output Formats
Text	Rubric scoring (helpfulness/groundedness), factuality checks, pairwise preference, refusal quality, rationale tagging	Abaka Forge	JSONL, CSV, TSV; rubric schemas; per-case score + notes
LLM RLHF	Preference ranking, DPO/SFT-ready labels, instruction-following evaluation, policy compliance scoring	Abaka Forge	JSONL (prompt, chosen, rejected), CSV exports, reviewer audit logs
Image	VQA scoring, caption quality evaluation, grounding checks, hallucination tagging	Abaka Forge	JSONL with image references, rubric scores, bounding references where applicable
Video	Video spatial reasoning evaluation, temporal consistency scoring, step-by-step instruction adherence	Abaka Forge	JSONL/CSV with clip IDs, timestamps, rubric dimensions, reviewer notes
3D/4D Point Cloud	Scene understanding evaluation, object presence/attributes scoring, robustness slices by environment	Abaka Forge	Structured reports + JSON/CSV summaries; task-specific schemas
LiDAR + Camera fusion	Cross-sensor grounding evaluation, perception vs reasoning error tagging, consistency checks	Abaka Forge	JSON/CSV score summaries; linked sensor-frame references
Audio	ASR quality scoring, intent understanding, multilingual robustness, safety checks for voice agents	Abaka Forge	JSONL/CSV with transcript, timestamps, rubric scores, error categories

Success Story

A frontier model lab shipping an enterprise LLM with tool calling

Challenge

A frontier model lab was preparing to roll out a new enterprise LLM release that introduced stronger tool calling (structured JSON actions) and expanded multilingual support. Their internal evaluation was fragmented: a handful of prompts per engineer, inconsistent rubrics, and no stable regression gate. Stakeholders disagreed on whether the new checkpoint was actually better—especially on long-tail safety issues, prompt injection attempts against the retrieval layer, and multi-turn conversations where the model had to plan, call tools, and recover from errors. The team needed a repeatable, defensible LLM model evaluation program that could run at release cadence without derailing engineering velocity.

Approach

Abaka worked with the lab to define a scorecard aligned to a 6-dimension framework—Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability. We built a versioned evaluation suite that combined (1) objective benchmarks for core tasks, (2) model-as-judge sweeps for large-scale comparisons, and (3) calibrated human evaluation on anchor sets where nuance mattered (policy refusals, multilingual tone, and correctness under tool constraints). We added targeted red-team scenarios including prompt injection probes, jailbreak variants, and tool misuse attempts. All workflows were orchestrated in Abaka Forge with QA sampling and adjudication to keep judgments consistent and auditable. Finally, we delivered slice reporting by locale, workflow type, and severity so the team could prioritize fixes that mattered most for enterprise deployments.

Results

With a stable suite in place, the lab moved from subjective debate to measurable release gates. The evaluation surfaced that the new model improved general helpfulness but introduced a specific regression in tool argument validation under multilingual prompts—something their original prompts didn’t catch. Abaka’s categorized failure cases gave engineers concrete reproduction steps and a clear path to mitigation (schema tightening, improved system prompts, and tool error recovery patterns). The lab then re-ran the exact same suite on the patched build to verify improvements and lock a go/no-go decision with evidence that could be shared across engineering, product, and risk stakeholders. - **3,000+** evaluation cases executed across core workflows and adversarial probes - **6** scorecard dimensions tracked with slice-level reporting by locale and workflow - **2** release iterations validated with the same versioned regression gate

3,000+

Evaluation cases scored

Scorecard dimensions tracked

Release iterations validated

By the Numbers

Evaluation framework across accuracy, robustness, efficiency, safety, tool calling, and UX

Red Teaming starting price point ($8 per evaluation)

Defensive Coding evaluation starting price point ($15 per evaluation)

0.20

Abaka Forge platform credits ($0.20 USD each)

What Customers Say

We needed an evaluation program our engineers could trust and our risk stakeholders could sign off on. Abaka helped us move from scattered prompts to a versioned suite with clear rubrics, adjudication, and repeatable exports. The weekly readouts made regressions obvious, and the failure taxonomy turned evaluation into an engineering backlog instead of a debate.

Head of Applied AI Enterprise SaaS Company

Our biggest pain was tool calling: the model looked good in demos, then failed on argument validation and recovery in production-like flows. Abaka’s evaluation traced failures step-by-step, scored each stage, and highlighted where guardrails were missing. The result was a practical release gate we now run before every deployment.

ML Platform Lead Fintech Company

We operate in multiple languages and couldn’t rely on a single English test set. Abaka built slice coverage by locale, calibrated reviewers, and kept scoring consistent across markets. The outputs were structured enough to plug into our internal dashboards, and the examples made it easy to fix issues without guesswork.

Product Director, AI Experiences Global Retail Company

Safety evaluation was our blocker. We needed red-teaming that reflected real misuse, not generic checklists, and we needed traceable evidence. Abaka delivered categorized adversarial cases, severity tags, and reproduction steps—then helped us validate mitigations by re-running the same suite. That discipline reduced our incident anxiety significantly.

Security Engineering Manager Cloud Infrastructure Company

Why Choose Abaka

Evaluation built for frontier AI—without competing incentives

Abaka is a trustworthy data partner for frontier AI. We never build models that compete with you—your evaluation data, prompts, outputs, and labels are exclusively yours and are never repurposed, resold, or shared. Because Abaka is self-funded and profitable (founded in 2019), there’s no acquisition pressure that can distort incentives. You get a long-term evaluation partner focused on your success and your IP.

6-dimension framework that maps to production risk

Generic “accuracy” scores don’t predict production outcomes. Abaka evaluates across six dimensions: Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability. This keeps teams aligned and helps you communicate model readiness to stakeholders with different priorities—engineering, product, security, and compliance—using one consistent scorecard.

Human + automation balance via Abaka Forge

Evaluation needs both depth and scale. Abaka Forge orchestrates routing, QA, adjudication, and exports while enabling large-model automation for volume sweeps. Humans focus on nuanced cases—domain correctness, policy interpretation, multilingual tone, and adversarial behaviors—while automation accelerates broad comparisons. This hybrid approach makes evaluation sustainable at release cadence without sacrificing defensibility where it matters.

Secure, governed operations for enterprise workflows

Abaka operates with SOC 2 and ISO 27001-aligned controls and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and maintain full IP provenance for data. For evaluation programs, this means your prompts, outputs, and labeled artifacts are handled with the same rigor you expect from enterprise vendors—enabling internal audits and reducing risk when evaluation includes sensitive workflows.

Specialized reviewers across hard domains

Hard evaluation requires the right expertise. Abaka supports scholar-network domains including mathematics, coding, medicine, law, business, and languages—so your model is judged by people who can actually verify correctness. This improves rubric quality and reduces label noise, resulting in more actionable feedback. Instead of “seems good,” you get concrete error categories and examples that engineers can fix.

Scale and global coverage for multilingual and long-tail testing

Abaka supports large-scale evaluation needs with a global footprint and vertically specialized reviewers across many regions. Whether you’re testing a new language launch, comparing multiple model candidates, or running continuous monitoring, we help you scale evaluation without over-hiring. You can add locales, domains, and adversarial cases over time while keeping the suite versioned—so improvements are measurable and regressions are caught early.

Frequently Asked Questions

Expand all

How much does LLM model evaluation cost?

Pricing depends on methodology and volume. As reference points: Red Teaming starts at $8 per evaluation and Defensive Coding starts at $15 per evaluation. For broader human evaluation, we scope rubrics, reviewer expertise, and sampling strategy to match your release cadence. We’ll propose a clear unit-cost plan (and optional Abaka Forge credits) after a short kickoff.

How fast can you start an evaluation project for our LLM?

Most teams can start within days. We typically finalize scope and rubrics in Day 0–3, build and calibrate the initial test set in Week 1–2, then execute the first scored run by Week 2–3. If you already have a test set, we can move faster by focusing on calibration, QA, and reporting.

What modalities and output formats do you support for evaluation?

We support text, RLHF preference data, image, video, audio, and multimodal workflows including tool-calling traces. Deliverables commonly include JSONL and CSV exports with per-case scores, rubrics, rationales, and slice tags. If your pipeline expects specific schemas, we can align outputs to your internal conventions for easy CI or dashboard integration.

How do you ensure evaluation accuracy and consistency across reviewers?

We use calibrated rubrics, reviewer training, QA sampling, and adjudication for disagreements. We typically create an anchor set of cases to measure inter-reviewer consistency and keep scoring stable over time. For scale, we can add model-as-judge pipelines, but we validate judge reliability against human-labeled anchors so results remain defensible.

Can you handle security and sensitive data in our evaluation prompts and outputs?

Yes—Abaka operates with SOC 2 and ISO 27001-aligned controls and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and controlled access. We can also work with sanitized or de-identified logs and restrict evaluator visibility based on need-to-know, depending on your internal policies.

Do you support multilingual LLM evaluation and localization quality checks?

Yes. We evaluate multilingual quality, tone, and safety behavior by locale, not just by translating an English test set. We can build locale-specific prompts, rubrics, and slices, then calibrate reviewers so scoring remains comparable across languages. Reporting can break down results by language, region, and workflow so you can target improvements efficiently.

How is Abaka different from other LLM evaluation vendors or crowd platforms?

Abaka combines a structured 6-dimension evaluation framework with calibrated human reviewers and scalable automation in Abaka Forge. We also never build models that compete with you—your evaluation data is exclusively yours and is never repurposed. That governance plus repeatable ops (QA, adjudication, versioning) is designed for production release gates, not one-off studies.

Can we request changes to rubrics or add new test cases after kickoff?

Yes. We version rubrics and datasets so you can evolve coverage without losing comparability. When you add new workflows, tools, or languages, we’ll propose an update plan that includes calibration and QA to keep scoring consistent. We can also maintain a stable “core regression set” while expanding an “exploration set” over time.

Do you offer a pilot for LLM model evaluation before a longer engagement?

Yes. Many teams start with a pilot focused on one or two workflows (e.g., RAG Q&A and tool calling) plus a targeted red-team slice. The pilot typically produces a versioned test set, calibrated rubric, first scorecard, and a prioritized defect list. After the pilot, you can extend to continuous monitoring or broader modalities.

Who owns the evaluation datasets, prompts, and labels you produce?

You do. Your data is exclusively yours and is never repurposed, resold, or shared. We can deliver full exports (cases, rubrics, scores, rationales, and audit logs) so your team can store them in your own systems. If you want, we can also keep a mirrored, access-controlled copy for ongoing operations.

What tools do you use to run evaluations and manage workflows?

We use Abaka Forge to orchestrate evaluation workflows: task routing, reviewer calibration, QA sampling, adjudication, and exports. We can also integrate with your existing stack via structured JSONL/CSV outputs and versioning. If you have internal evaluation harnesses, we can adapt to your formats while keeping scoring and QA consistent.

What is the minimum project size for LLM model evaluation?

There isn’t a single minimum, but meaningful evaluation typically needs enough cases to cover key workflows and slices (locales, domains, risk areas). Many teams begin with a few hundred to a few thousand cases to establish a reliable baseline and regression gate. We’ll recommend a right-sized plan based on your release cadence and risk tolerance.

Ready to Get Started?

If your team is shipping an LLM to real users, you need evaluation you can defend—repeatable suites, calibrated rubrics, and scorecards that translate directly into engineering action. Abaka can stand up an end-to-end LLM model evaluation program, from red-teaming and tool-calling tests to multilingual and multimodal coverage, with secure operations and clear deliverables. Talk to an Expert at business@abaka.ai — Human Intelligence — Data for Frontier AI. Annotate the Present. Train the Future.