AI ML Consulting: 2026 Production Guide

AI ML consulting in 2026 is the strategic and technical practice of designing, building, and deploying production-grade artificial intelligence and machine learning architectures into business operations. Specialized consulting partners help enterprise software companies build custom autonomous agents, deterministic retrieval-augmented generation (RAG) pipelines, and fine-tuned machine learning models that generate tangible operational ROI.

TL;DR

Modern ai ml consulting bridges the gap between theoretical artificial intelligence research and resilient production software. Rather than building disposable prototypes or fragile wrappers, elite consultancies architect compound AI systems, autonomous agent workflows, and open-standard integrations using Anthropic’s Model Context Protocol (MCP). For B2B SaaS founders and technical leaders, partnering with an execution-focused consultancy drastically shortens time-to-market, eliminates high-touch operational debt, and ensures full ownership of proprietary intellectual property.

Key Takeaways: Strategic AI ML Consulting in 2026

Executive Summary & Core Insights

• Production-Grade Delivery Over PoCs: Enterprise artificial intelligence has evolved beyond experimental notebooks into resilient, deterministic systems powered by continuous evaluation pipelines and small language models (SLMs).
• Bypassing the Engineering Bottleneck: Sourcing, vetting, and onboarding specialized in-house machine learning personnel often requires extended hiring cycles; external consulting engagements allow companies to deploy production infrastructure in weeks rather than quarters.
• Standardization via Model Context Protocol (MCP): Modern architecture leverages Anthropic’s open-standard Model Context Protocol to connect private data sources and APIs to reasoning engines without proprietary vendor lock-in.
• Autonomous Workflows Replace Brittle Automation: Specialized firms replace fragile, multi-step webhook triggers with self-healing autonomous agents capable of dynamic tool routing and deterministic exception handling.
• Deterministic Evaluation: Experienced consultancies implement automated evaluation harnesses that benchmark semantic retrieval, output schema adherence, and factual accuracy against ground truth before any model deployment.
• Measurable Financial Return: Engineering engagements deliver high ROI by targeting operational bottlenecks, reducing LLM token inference expenses through prompt caching and semantic routing, and creating proprietary product moats.

What is AI ML Consulting? Scope, Capabilities, and Modern Architecture

AI ML consulting is the professional service of engineering custom machine learning models, autonomous agent frameworks, and operational data pipelines to solve high-value enterprise problems.

Unlike conventional IT services that focus on static software engineering, AI consultancies specialize in non-deterministic systems, probabilistic reasoning, and high-throughput vector architectures.

┌─────────────────────────────────────────────────────────────┐
│               Compound AI System Architecture               │
├─────────────────┬─────────────────────────┬─────────────────┤
│   Input Layer   │   Orchestration Layer   │ Execution & IP  │
├─────────────────┼─────────────────────────┼─────────────────┤
│ • Unstructured  │ • Dynamic Tool Routing  │ • MCP Servers   │
│   Data Ingestion│ • Prompt & Semantic     │ • Relational &  │
│ • User Intent   │   Caching               │   Vector DBs    │
│   Classification│ • Continuous Evals      │ • Fine-Tuned    │
│ • Guardrail API │   (DeepEval / Ragas)    │   Hosted SLMs   │
└─────────────────┴─────────────────────────┴─────────────────┘

The Shift from Experimental PoCs to Production Systems

In earlier development cycles, enterprise artificial intelligence initiatives frequently stalled at the proof-of-concept (PoC) phase. Fragile API wrappers and superficial chatbot interfaces failed under real-world enterprise volume, lacking validation layers, token governance, or reliable context retrieval. In 2026, the industry has transitioned permanently toward compound AI systems.

A compound AI system is an architectural paradigm where multiple interacting components—including retrieval models, specialized small language models, deterministic rules engines, and external API tool-calling interfaces—collaborate to solve complex tasks with high reliability. Rather than asking a single massive model to perform every stage of logic, modern consulting engagements decompose workflows into modular, auditable micro-tasks.

Modern engineering relies on automated evaluations (evals) integrated directly into continuous integration and deployment (CI/CD) pipelines. Using frameworks like DeepEval or Ragas, systems measure context recall, hallucination rates, and answer relevancy against historical ground-truth datasets. This systematic benchmarking ensures that business applications maintain strict reliability thresholds before reaching production end users. Off-the-shelf wrappers fail at scale because they lack these deterministic guardrails, structured JSON schema enforcers, and custom fallback routing mechanisms.

Core Pillars: Machine Learning, Generative Systems, and Autonomous Agents

Modern technical engagements span three core engineering disciplines:

1. Predictive Machine Learning: Utilizing classical regression, gradient boosting (XGBoost, LightGBM), and deep learning architectures for structured enterprise data. These pipelines power real-time churn prediction, customer lifetime value (LTV) forecasting, dynamic pricing algorithms, and high-precision lead scoring models.
2. Generative Systems & Production RAG: Constructing sophisticated domain-specific Retrieval-Augmented Generation architectures. Rather than naive vector lookups, production RAG in 2026 relies on hybrid retrieval (combining BM25 lexical search with dense vector embeddings), contextual document chunking, dynamic reranking models such as Cohere Rerank, and knowledge graphs that capture deep entity relationships.
3. Autonomous Agents: Engineering systems that do not merely generate text, but take autonomous actions across software ecosystems. These agents leverage tool-calling capabilities, self-correcting reasoning loops, and structured state machines to execute multi-step workflows like triaging code pull requests, managing complex customer onboarding, or reconciling financial transactions without human intervention.

The Role of Model Context Protocol (MCP) in Modern Consulting

A pivotal shift in modern systems engineering is the widespread adoption of the Model Context Protocol (MCP).

Model Context Protocol (MCP): An open-standard protocol introduced by Anthropic that standardizes how language models securely discover, query, and manipulate context across disparate external data sources and local developer environments.

Prior to MCP, engineering teams were forced to write bespoke, fragile point-to-point integration code whenever an LLM needed access to an internal Postgres database, a CRM platform, or an enterprise microservice. This pattern created technical debt and vendor lock-in. Specialized consultancies now construct modular, client-owned MCP servers that expose secure interfaces to company data. This open protocol decouples the reasoning engine from the underlying data warehouse. If an organization decides to switch foundational model providers, the entire data ingestion layer, tool suite, and authentication perimeter remain completely intact.

Evaluation Harnesses and Continuous Quality Assurance

A core differentiator of professional consulting is the implementation of continuous evaluation architectures. Probabilistic models produce varying outputs, meaning traditional unit tests asserting static strings are insufficient. Consultancies design synthetic test suites and golden datasets containing hundreds of edge-case scenarios.

During every pull request, the evaluation harness executes automated runs checking for semantic drift, tone consistency, schema validity, and context precision. If an updated prompt or model checkpoint causes context recall to drop below predefined thresholds, the deployment pipeline halts automatically. This rigor transforms generative models into stable software components that enterprise compliance officers and technical leaders can trust.

Data Infrastructure, Embeddings, and Vector Management

High-performing intelligence systems depend entirely on clean underlying data pipelines. Consultancies audit existing data stores, establishing transformation layers that clean, normalize, and chunk unstructured data. Document chunking strategies are customized based on document topology—using recursive character splitters for legal contracts, semantic paragraph chunking for documentation, and tabular markdown transformers for financial spreadsheets.

These chunks are indexed using multi-stage vector search engines like pgvector, Qdrant, or Pinecone, paired with sparse BM25 indices. By pairing dense semantic retrieval with sparse keyword matching, production systems achieve superior precision when searching for acronyms, part numbers, and technical terminology that standard embedding models often overlook.

When Should B2B SaaS Founders Hire an AI ML Consulting Agency?

Deciding between external expertise and internal development requires an objective audit of technical capabilities, capital allocation, and current operational constraints. Many growing companies discover that custom engineering solves operational bottlenecks far more effectively than generic software additions.

Does manual data manipulation consume >30% of engineering/ops time?
 ├── YES ──> Hire Consulting Partner (Fast automation of high-touch debt)
 └── NO  ──> Are you embedding core AI features into your SaaS roadmap?
              ├── YES ──> Is your internal ML engineering team fully hired?
              │            ├── YES ──> Build In-House
              │            └── NO  ──> Hire Consulting Partner (Bypass hiring delay)
              └── NO  ──> Rely on Off-the-Shelf SaaS Solutions

Eliminating High-Touch Manual Workflows & Operational Debt

When specialized software and operations teams spend substantial portions of their day handling manual data entry, customer triage, unstructured document processing, or repetitive onboarding configurations, the company accumulates operational drag. While simple tools serve early-stage prototyping, they rapidly buckle under enterprise volume, lacking robust error handling, automated retry queues, and context awareness.

Specialized firms replace these fragile automations with self-healing agent pipelines. For instance, when an edge case occurs—such as a malformed vendor invoice or an ambiguous support escalation—an autonomous agent analyzes the system anomaly, requests clarifying structured input via a web interface, and completes the transaction deterministically. Founders can explore how these high-ROI systems are structured in this guide to AI for business automation.

Audit your systems: If legacy pipelines and fragile scripts are creating technical debt across your organisation, evaluate where automated workflows deliver tangible returns — Book the AI Opportunity Audit.

Embedding Defensible AI Features into Core SaaS Products

For software companies, customer expectations have shifted radically. Simple chatbot add-ons and thin API wrappers are no longer defensible product differentiators; enterprise buyers routinely reject applications that fail to provide strict data isolation, zero-data-retention compliance, and domain-accurate responses.

Building defensibility requires pairing proprietary customer interaction loops with specialized architectures. An experienced agency designs multi-tenant database infrastructures that preserve tenant-level encryption while allowing small, fine-tuned models to run efficiently across partitioned data stores. Engineering teams seeking to upgrade their platform capabilities often review specialized strategies for custom AI for business operations to design these proprietary data flywheels.

Architecting Scalable AI-Powered Growth & Outreach Engines

SaaS growth requires scaling both backend software capabilities and market acquisition funnels. Manual prospecting, generic email sequences, and manual content workflows yield diminishing returns. Consultancies help modern revenue teams construct automated inbound enrichment engines and algorithmic outbound systems.

By connecting proprietary data scrapers to language models via custom MCP servers, these growth engines automatically research incoming accounts, evaluate technical fit, match prospects against ideal customer profiles (ICPs), and generate contextualized messaging. Furthermore, technical consulting studios build programmatic workflows that convert technical documentation and user data into high-ranking search content. Founders looking to systematically modernize their customer acquisition pipelines can leverage frameworks for AI-powered marketing automation to replace manual marketing busywork.

Solving the Internal Talent and Execution Gap

The global shortage of seasoned machine learning engineers makes building an internal department expensive and time-consuming. Attracting senior practitioners who understand model quantization, vector optimization, and distributed agent orchestration requires extensive compensation packages and months of recruitment effort.

For early-stage and mid-market B2B SaaS companies, committing hundreds of thousands of pounds to permanent specialized payroll before validating an AI feature’s market demand creates significant financial risk. Engaging an external consultancy provides immediate access to production-tested architectures. This allows businesses to ship functional software, validate client utility, and establish commercial traction while maintaining lean operational structures.

Implementation Paths Compared: AI ML Consulting vs. In-House vs. Out-of-the-Box Tools

Founders must weigh the trade-offs of speed, operational cost, and architectural control when choosing how to build intelligent systems.

Strategic Trade-Off Analysis

Hiring an in-house machine learning team carries substantial hidden costs. Because a complete AI production unit requires diverse skill sets—a data engineer, an infrastructure specialist, and an applied researcher—assembling an internal team requires significant capital before shipping a single line of production code.

Conversely, stitching together multiple third-party SaaS platforms creates integration debt. Point solutions frequently deprecate endpoints, change terms of service, and silo mission-critical company data within proprietary ecosystems.

Intellectual Property Ownership and Data Governance

A critical danger of relying on closed third-party SaaS tools is the total forfeiture of intellectual property. When an enterprise configures its core business logic inside a proprietary cloud platform, it builds enterprise value for that vendor rather than its own balance sheet.

Reputable consulting partners provide complete intellectual property assignment upon milestone completion. Every custom script, fine-tuning training dataset, model checkpoint, vector embedding schema, and orchestration pipeline belongs strictly to the client. Additionally, modern consulting frameworks enforce Zero Data Retention (ZDR) agreements with frontier model providers, guaranteeing that proprietary customer data is never used to train external public models. Leaders evaluating agency operational models can compare structural nuances in this breakdown of an artificial intelligence automation agency.

Time-to-Value and ROI Horizons

Specialized agencies leverage reusable, battle-tested component libraries—such as modular semantic cache layers, token-budgeting middleware, and self-hosted evaluation engines. Consequently, an agency can transition a system from architectural blueprinting to live customer deployment in as little as 6 to 12 weeks.

In contrast, internal enterprise builds regularly suffer from prolonged scoping phases and hiring delays. The financial payback period for an external consulting engagement typically materializes within two to four quarters, manifested through the direct reduction of manual operational headcounts, lower customer churn, and optimized token inference costs. For smaller firms navigating initial investments, reviewing principles for AI automation for small business provides practical baseline expectations.

Total Cost of Ownership Across the Software Lifecycle

Evaluating total cost of ownership (TCO) requires looking past upfront engineering fees. Out-of-the-box tools often appear affordable initially, but their seat-based licensing models scale linearly with headcount, creating ballooning SaaS expenses as the organization expands.

Custom architectures built during a consulting engagement shift the commercial profile toward low-cost utility hosting (such as serverless container execution and direct token consumption). Once deployed, these systems process immense data volumes with negligible incremental costs. Furthermore, client-owned architectures can be modified, re-hosted, or expanded internally at any time without paying vendor penalties or migration fees.

The 5-Stage AI ML Consulting Framework for Production Delivery

Engineering enterprise artificial intelligence requires a disciplined, sequential delivery methodology. Leading consultancies use a five-stage framework to de-risk investments and ensure production reliability.

Stage 1: Feasibility Audit ──> Stage 2: Architecture Blueprint
                                          │
Stage 4: Production RAG & Agents <── Stage 3: Rapid PoC Sprint
        │
Stage 5: Deployment & Inference Optimization

Stage 1: Technical Feasibility and Data Readiness Audit

Every successful production deployment begins with an intensive data readiness audit. During this opening phase, consultants analyze client databases, unstructured document repositories, API rate limits, and latency requirements. Many organizations discover that their existing unstructured documents require advanced extraction pipelines before any model can consume them.

The audit evaluates data quality across four specific vectors:

1. Completeness: Are the necessary entity attributes present without widespread missing fields?
2. Consistency: Does historical data adhere to predictable formatting, or do legacy discrepancies require normalization?
3. Volume: Is there sufficient domain data to support fine-tuning or contextual grounding via vector embeddings?
4. Security & Privacy: Are sensitive attributes (such as personally identifiable information) clearly isolated, tagged, and ready for redaction?

Stage 2: Architecture Blueprinting and Model Selection

Following the audit, consultants formulate the system architecture blueprint. A central element of this blueprint is foundational model selection. Consultancies evaluate the trade-offs between frontier closed models (such as Claude 3.5 Sonnet or GPT-4o) and open-weights small language models (such as Llama 3.3 or Mistral).

While frontier models excel at complex multi-step reasoning, fine-tuned open-weights models running on dedicated cloud infrastructure routinely deliver sub-50ms latency at a fraction of the operating cost. The architecture blueprint explicitly outlines network topologies, database schemas, API authentication boundaries, and state management strategies.

Stage 3: Rapid Validation and Deterministic Prototyping

During Stage 3, the consultancy develops an end-to-end, low-latency prototype within two to four weeks. This prototype intentionally bypasses complex user interfaces to focus strictly on verifying the core reasoning loop with human-in-the-loop validation.

By focusing on core input-output transformations and prompt orchestration early, engineering teams identify logical edge cases and semantic ambiguities before building front-end user experiences. This rapid validation sprint confirms whether the underlying data supports the target business objective without committing massive capital.

Stage 4: Production-Grade RAG, Tool Use, and Agent Orchestration

Once the prototype validates the core hypothesis, engineering transitions to Stage 4: production orchestration. Here, the consultancy implements state-of-the-art information retrieval systems:

1. Contextual Document Chunking: Splitting raw enterprise data based on semantic boundaries rather than arbitrary token counts.
2. Hybrid BM25 and Vector Search: Indexing data simultaneously into inverted lexical indexes and dense vector embeddings.
3. Cross-Encoder Reranking: Running retrieved candidate context chunks through a cross-encoder model to discard irrelevant context before it hits the LLM context window.
4. Structured JSON Validation: Enforcing deterministic response schemas via libraries like Pydantic or native model constrained decoding.
5. Continuous Evaluation: Running automated tests against curated ground-truth datasets to detect model regressions before deployment.

Stage 5: Containerized Deployment, Observability, and Inference Optimization

The final stage migrates the validated system into containerized cloud infrastructure (such as AWS Bedrock, GCP Vertex AI, or private Kubernetes clusters). Telemetry tools—including Langfuse or OpenTelemetry—are connected to trace agent tool calls, latency bottlenecks, and real-time token expenditures.

Client Query ──> Semantic Vector Cache (Cache Hit? Return Response in 15ms)
      │ (Cache Miss)
      ▼
 Model Router ─── Low Complexity ───> Fast SLM ($0.05 / 1M tokens)
      │
 High Complexity
      │
      ▼
 Frontier Model + MCP Tools ($3.00 / 1M tokens)

Inference optimization is applied aggressively to drive down ongoing operating expenses:

• Prompt Caching: Utilizing prompt caching features across long context blocks and system instructions, reducing input token costs significantly.
• Semantic Vector Caching: Storing previous question-and-answer pairs in an in-memory vector store such as Redis. If an incoming query is semantically identical to a recent request, the system returns the cached response in milliseconds without invoking an LLM.
• Intelligent Model Routing: Sending simple categorization and extraction queries to low-cost small language models, while reserving expensive frontier models exclusively for multi-step analytical reasoning.

Deploying intelligent routing and prompt caching allows systems to handle high enterprise query volumes while keeping operational inference bills predictable.

Explore custom implementations: When evaluating whether to build internal platforms or configure specialized autonomous agents for your commercial operations — Explore our services.

How to Vet and Select an AI ML Consulting Partner in 2026

Choosing the wrong technical partner results in discarded code, security vulnerabilities, and months of wasted market momentum. Founders must rigorously evaluate candidate firms on concrete engineering capabilities rather than marketing claims.

Technical Competency vs. Superficial Prompt Engineering

The market contains many traditional digital agencies rebranding themselves as AI consultancies without deep technical capabilities. Founders can separate surface-level prompt engineering from deep systems architecture by probing candidate agencies on backend fundamentals:

• Asynchronous Queue Management: How does the consultancy handle long-running agent workflows? Look for deep familiarity with asynchronous distributed task queues like Celery, Temporal, or Redis Streams.
• Model Context Protocol (MCP) Experience: Inquire about their practical experience building and hosting custom MCP servers. An agency should clearly articulate how they decouple tools from model runtimes.
• State Machine Design: How do they ensure agents do not get trapped in infinite reasoning loops? Top firms construct deterministic finite-state machines (FSMs) with strict exit parameters and automated fallbacks.
• Engineering Stack: Be wary of consultancies proposing complete enterprise backends built entirely on visual no-code tools. While valuable for early mockups, no-code stacks cannot support custom evaluation suites, programmatic token-budgeting, or low-latency enterprise features.

Data Security, Compliance, and Enterprise Governance

Enterprise software buyers demand ironclad data privacy. Your consulting partner must design pipelines that maintain complete compliance with global data privacy frameworks:

• Zero Data Retention Policies: The agency must configure all third-party API contracts to ensure client data is never stored on external servers or used for model training.
• Role-Based Access Control (RBAC): Ensure that vector embeddings and knowledge retrieval systems inherit the client’s underlying data permissions. A sales representative querying the internal system must never retrieve unredacted executive payroll records from the vector store.
• Compliance Standards: Custom pipelines must be engineered to align with SOC 2 Type II, GDPR, and HIPAA requirements from day one, including end-to-end encryption in transit and at rest.

Commercial Alignment: Fixed-Scope Sprints vs. Dedicated Retainers

Consulting firms generally operate under two commercial structures: fixed-scope milestone sprints or dedicated retainers.

For well-defined systems—such as an automated document-processing engine or an initial MCP server integration—fixed-scope sprint contracts are optimal. They define explicit functional acceptance criteria, concrete latency benchmarks, and strict delivery dates. For ongoing product evolution, embedding proprietary AI across multiple software modules, or continuous inference optimization, a dedicated retainer model provides the agility required to iterate rapidly alongside internal product managers.

Regardless of commercial structure, the consulting partner should provide a comprehensive knowledge transfer plan, thoroughly training internal developers to manage, evaluate, and scale the codebase independently.

Code Ownership and Knowledge Transfer Standards

A dependable consulting partner must never treat custom engineering as a mechanism to create ongoing operational lock-in. From the initial repository commit, all source code, deployment scripts, Docker containers, infrastructure-as-code manifests, and dataset transformations must reside inside your corporate repositories.

Comprehensive architectural handoffs include detailed API documentation, runbooks for model evaluation, and clear instructions for updating system prompts and retrieval parameters. When an engagement concludes, your internal technical personnel should possess the full capability to operate, debug, and expand the production system autonomously.

Choosing the right partner for ai ml consulting ensures that your business deploys durable, secure, and commercially viable artificial intelligence systems that scale alongside your long-term roadmap.

How Techno Believe Can Help

Adopting artificial intelligence in 2026 presents B2B SaaS founders and UK business leaders with difficult architectural choices. Between escalating API inference costs, fragile prototype scripts, and uncertain return on investment, deciding where machine learning creates genuine operational leverage requires rigorous technical analysis rather than speculative experimentation.

Techno Believe (Techno Believe Solutions Ltd, London) is an AI automation consultancy for UK businesses: it audits where AI will pay off, then builds the workflows and web products.

To identify high-value automation opportunities and establish clear commercial feasibility before writing code, Book the AI Opportunity Audit.

FAQ

What does an ai ml consulting engagement typically cost in 2026?

Consulting costs vary widely depending on technical scope and system maturity. Focused discovery audits and architectural evaluations typically require smaller initial investments, whereas complete production agent builds and custom RAG infrastructures scale higher depending on data pipeline complexity. Engagements are generally structured as fixed-fee milestones or sprint-based deliverables to maintain commercial transparency.

How long does an ai ml consulting project take to reach production?

A standard consulting engagement typically delivers a functional prototype within two to four weeks. Full production deployments—including hybrid vector retrieval, automated evaluation harnesses, containerized cloud infrastructure, and data pipeline integrations—usually require between six and twelve weeks. Timelines depend heavily on the structure and accessibility of the underlying enterprise data.

What is the difference between an AI ML consultancy and a traditional software agency?

Traditional software agencies specialize in deterministic web applications, static user interfaces, and relational database systems. An AI ML consultancy specializes in non-deterministic systems, probabilistic reasoning, vector search architectures, automated evaluation benchmarks, and autonomous agent orchestration. Consultancies solve complex edge cases inherent to probabilistic machine learning that standard software patterns cannot handle.

Who owns the intellectual property and code developed during an engagement?

Reputable consultancies ensure that one hundred percent of intellectual property belongs directly to the client. All source code, data preprocessing scripts, vector embeddings, fine-tuned model checkpoints, and configuration manifests are transferred entirely to the client’s repositories. Client proprietary data is protected under zero-retention agreements and is never used to train external models.

How does an AI ML consultancy prevent hallucinations in production RAG systems?

Consultancies mitigate hallucinations by replacing naive vector lookups with hybrid search, contextual chunking, and cross-encoder rerankers that supply only relevant context to the model. In addition, systems enforce deterministic JSON schemas using validation libraries like Pydantic and maintain continuous automated evaluation harnesses to test outputs against ground-truth benchmarks. Fallback routines ensure the system gracefully handles ungrounded queries rather than fabricating answers.

Can an AI ML consultancy assist in reducing ongoing model inference costs?

Yes, specialized consultancies apply multiple inference optimization techniques that lower continuous API operational expenses. By implementing prompt caching, semantic vector caching in Redis, and dynamic model routing, systems route simple tasks to lightweight models while reserving frontier reasoning models for complex requests. These optimizations reduce recurring token usage while improving end-user response latency.

Sources

• Model Context Protocol Specification — Official documentation and technical standards for Anthropic’s open-standard Model Context Protocol.
• Stanford HAI AI Index Report — Annual research study tracking global machine learning advancements, adoption trends, and economic impacts.
• Google Cloud Architecture Framework: Machine Learning — Architectural best practices for deploying resilient, enterprise-scale machine learning workloads.
• DeepEval Testing Framework Documentation — Technical overview of continuous evaluation suites and metric harnesses for LLM applications.
• Ragas Evaluation Standards — Framework specifications for evaluating retrieval-augmented generation pipelines and semantic search quality.
• Cohere Rerank Documentation — Technical guides detailing two-stage retrieval optimization using neural cross-encoders.

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