Problems we solve · 2026

Six problems. Solved, not piloted.

These are the six enterprise AI solutions DILR.ai is hired to build most often in 2026, each answering a problem a business already has: developer productivity lost to code review, LLM costs climbing while token prices fall, agents that break in production, knowledge nobody can find, follow-up that never happens, and calls nobody answers. Every use case below states the problem with its evidence, then what we do about it, from our base in London.

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01 The problem

Work falls through the gaps between your tools.

The call ends and the CRM note never gets written. The form comes in on Friday evening and the callback happens Monday. Nobody is lazy: the work lives in the seams between systems that were never joined up, and it depends on somebody remembering. Speed is the part that costs you. The Lead Response Management study led by Oldroyd at MIT Sloan, which is almost universally miscredited to Harvard Business Review, found contact rates fall away sharply within the first hour.

What we do. We wire task-specific agents into the moment work actually happens, so the CRM record, the customer text and the callback task fire in the same minute the call ends. Gartner forecasts task-specific AI agents in 40% of enterprise applications by the end of 2026, up from under 5% a year earlier. Which of yours are worth automating is a decision before it is a build, which is what the Placement Diagnostic settles, alongside the list of things not to automate.

  • Post-call flows: CRM records, texts and callback tasks, automatically
  • Contact forms that trigger a real callback, not an autoresponder
  • Brand-locked content operations through DILR Studio
  • Starts with Placement Diagnostic
  • Typically 4 to 6 weeks, fixed fee

Find what is worth automating →How DATS works →

A business automation flowA call ending or a form being submitted triggers an automation agent, which fans out into three follow-up actions at once: a CRM record, a text back to the customer, and a callback task for the team. No step waits for a human to remember it.TRIGGERCall ends, or aform comes inAGENTAutomationagentSAME MINUTECRM recordText to thecustomerCallback taskEVERY CALL, EVERY FORM. NOTHING RELIES ON MEMORY.
Fig. 01 One trigger, three follow-ups, no one remembering

02 The problem

Developer productivity rose. Delivery did not.

Developer productivity is the most measured problem in enterprise AI right now, and the numbers are stark. The constraint is code review, not typing. Faros AI's telemetry across 22,000 developers in more than 4,000 teams found tasks completed per developer up 33.7%, and at the same time median pull request review time up 441%, incidents per pull request up 242.7%, and 31% more pull requests merging with no review at all. Google's DORA 2025 report, surveying around 5,000 developers, found the same shape: AI adoption correlates positively with throughput and negatively with stability, in the same teams, at the same time.

What we do. The bottleneck moved. It is no longer writing the code, it is knowing what to write and proving what was written is correct, so that is where we work: automated code review gates, evaluation harnesses that run on every change, and observability that makes a regression traceable instead of mysterious. DORA's own conclusion is the one we design around, that AI amplifies whatever capability a team already has rather than supplying the capability. Widening the review stage is what converts individual speed into shipped software, and the full AI developer productivity breakdown sets out the four moves that do it.

  • Code review treated as the constraint on developer productivity, not an afterthought
  • Evaluation harnesses that gate the merge, so speed does not cost stability
  • Delivery measured at the team level, where the gain actually shows up
  • Starts with Execution Office
  • Typically 12+ months embedded

Read the full breakdown →See the Execution Office →

Why more code does not mean faster deliveryTwo stages compared. Writing code speeds up when developers adopt AI. The review and verify stage does not, so a queue of unreviewed pull requests builds up between them and absorbs the gain. Delivery to production stays flat while incidents rise. Widening the review stage is what unblocks it.WRITEAI writesmore codeREVIEW AND VERIFYSame reviewcapacityDelivery,unchangedTHE QUEUE THAT FORMSunreviewed PRsCODE WRITTENTIME SPENT IN REVIEWTHE BOTTLENECK, NOT THE TYPING
Fig. 02 Output rises, the review stage does not, and the queue absorbs the gain

03 The problem

LLM costs climb while token prices fall.

Per-token prices have dropped steeply and enterprise LLM costs have gone up anyway, because usage grows faster than unit prices fall and nobody owns the total. It lands in a year when margins are already thin: the Conference Board's 2026 C-Suite Outlook, surveying 1,732 executives, puts a fragile economy and cost pressure at the top of the risk list. An AI line item that grows every month without anyone able to explain why is a governance problem before it is a technical one.

What we do. The fixes are unglamorous and they work. Route most queries to a small model and keep the frontier model for the hard few. Cache the repeated prompt prefix, which on Anthropic's published pricing takes 90% off cached input tokens. Cap what an agent may spend per session. Stop retrieval stuffing whole documents into prompts when a paragraph would do. Where the work is regulated, a small private model like Dilr Mira removes the per-token meter altogether by running on your own hardware.

  • Model routing, small-first, frontier reserved for the hard few
  • Prefix caching and hard per-session agent budgets
  • Right-sized retrieval, so the knowledge base stops inflating prompts
  • Starts with Placement Diagnostic
  • Typically 4 to 6 weeks, fixed fee

See Dilr Mira, the private SLM →Get your AI spend reviewed →

How routing and caching cut the token billTwo lanes compared, illustratively. Sending every query to a frontier model produces a long cost bar. Routing most queries to a small model, keeping the frontier model for the hard few, and caching the repeated prompt prefix produces a much shorter one. The exact ratio depends on your traffic.EVERY QUERY ON A FRONTIER MODELrelative spendROUTED, SMALL MODEL FIRSTspendSmall model,most queriesFrontier model,the hard fewCached prefix,repeat inputILLUSTRATIVE. THE RATIO DEPENDS ON YOUR TRAFFIC MIX.
Fig. 03 Routing and caching against a single-model baseline

04 The problem

It demos beautifully. Then it meets production.

Almost every organisation now has an agent that worked in the demo and cannot be trusted with real customers. The reason is rarely the model. It is everything around the model: no evaluation suite, so nobody can say whether a change made it better or worse; no permission boundaries, so it cannot be allowed to act; no observability, so when it goes wrong the investigation starts from nothing. That surrounding system now has a name, harness engineering, and it is where reliability actually lives.

What we do. We build the harness before the pilot is allowed to become production. How much difference it makes is measurable: in March 2026 the LangChain team moved their coding agent from 30th to 5th on Terminal Bench 2.0 without changing the underlying model at all, purely by re-engineering the harness. On our side that is DATS stage 04, run inside the Execution Office, with the governance and RACI from the Operating Model stage behind it.

  • Evaluation harnesses that gate the production cutover
  • Tool and permission design, so an agent can act without risk
  • Observability: every agent run traceable after the fact
  • Starts with Operating Model
  • Typically 6 to 10 weeks

See the Operating Model →Read our approach →

An agent harness around a modelA model sits at the centre. Around it, connected in a ring, sit the six parts of the harness: evals, tools, permissions, observability, memory and context. The harness is the part you engineer; the model is the part you swap.ModelEvalsToolsPermissionsObservabilityMemoryContextTHE HARNESSSWAP THE MODEL. KEEP THE HARNESS.
Fig. 04 The harness around a model: the six parts you own

05 The problem

The answer exists. Nobody can find it.

The policy is written down, the price was agreed in an email, the procedure is in a PDF somebody saved in 2023. McKinsey puts the cost of that at roughly 1.8 hours a day per employee, about 9.3 hours a week, spent searching for and gathering information: close to a quarter of the working day, before anyone has done the job they were hired for. Meanwhile the same questions get asked of the same three people, who answer from memory and occasionally get it wrong.

What we do. Every business already owns its best training data. We index it once and answer from it with the source cited, so a wrong answer traces back to a document you can fix rather than a guess you cannot. That citation is the whole point: an uncited answer is unauditable. Dilr Voice answers live phone calls from exactly this kind of knowledge base, and Dilr Mira does the equivalent for clinical documents without the data ever leaving your environment.

  • One index over the documents you already have
  • Cited answers: traceable, auditable, correctable
  • Scoped retrieval, so the knowledge base does not inflate the bill
  • Starts with Dilr Voice
  • Typically from $0.14/min, $10 trial credits

Hear it answer real calls →Common questions →

A company knowledge base with cited answersPolicies, price lists and FAQs flow into one indexed knowledge base. A question comes in and the answer comes back grounded, with the source document cited, so a wrong answer can be traced and fixed instead of guessed at.WHAT YOU ALREADY HAVEPoliciesPrice listsFAQs, emailsOne indexedknowledge baseGroundedanswerSOURCE: PRICE-LIST.PDFANSWERS TRACE TO A DOCUMENT, NOT A GUESS.
Fig. 05 Scattered documents to one answer, with the source cited

06 The problem

The phone rings out, and the lead is gone.

For a clinic, an estate agent or a trades business, the unanswered call is not a service issue, it is the revenue leak. Zoopla, with YouGov in November 2025, asked 1,000 UK homeowners who had sold in the previous five years what they expect: a quarter said 60 minutes is the maximum acceptable reply time, two-thirds expect a reply within four hours, and 63% prefer to be contacted by phone. Rightmove's audit of more than 200 UK agents found 61% replied within 24 hours and only around 30% responded to a valuation request at all.

What we do. A voice agent answers every inbound call at any hour, works out why the person is calling, answers from your own documents, books the appointment and transfers to a human the moment one is needed. Outbound campaigns run inside TCPA, GDPR and DNC rules. Pricing is published rather than quoted: $0.14 a minute, $10 of trial credits, no card, so a small business can be live the same day and find out whether it works before committing to anything.

  • Inbound frontdesk: answers, books, routes, transfers
  • Vertical builds for clinics and estate agents
  • Multi-agent handoff that keeps the caller's context
  • Starts with Dilr Voice
  • Typically from $0.14/min, live the same day

What an AI receptionist costs →Compare voice AI platforms →

An AI voice agent pipeline on one callAn inbound call, any hour, reaches a frontdesk agent. The call hands off to a booking agent with full context, and transfers to a human when the call needs one. One conversation, specialised agents, no repeated questions.24/7 INBOUNDFrontdeskagentBookingagentYouHANDOFF KEEPS CONTEXT. NO REPEATED QUESTIONS.ANSWERS FROM YOUR KNOWLEDGE BASE, BOOKS REAL SLOTS.
Fig. 06 One call, specialised agents, a human when it matters

Quick answers

The questions buyers actually ask.

Short, self-contained answers on enterprise AI use cases, what they cost and which to start with. Every figure names its source. Each answer links to the page carrying the detail.

What problems does AI actually solve for a business in 2026?

The six enterprise AI solutions we are asked for most often answer manual follow-up falling through the gaps between tools, a developer review queue that swallows AI speed gains, an AI bill that climbs while token prices fall, agents that demo well and fail in production, knowledge nobody can find, and unanswered calls. Each is a business problem with a measurable cost, not a technology to adopt. The Placement Diagnostic ranks which of yours is worth solving first, and which to leave alone.

Why has AI not made our developers ship faster?

Because the bottleneck moved rather than disappeared. Faros AI's telemetry across 22,000 developers in over 4,000 teams found tasks per developer up 33.7% while median pull request review time rose 441%, incidents per pull request rose 242.7%, and 31% more pull requests merged with no review at all. Google's DORA 2025 report, about 5,000 developers, found AI adoption correlates positively with throughput and negatively with stability simultaneously. The fix is widening review and verification, which is what the Execution Office builds.

How do you reduce LLM and token costs?

Route most queries to a small model and reserve the frontier model for the hard few, cache the repeated prompt prefix (Anthropic's published pricing takes 90% off cached input tokens), cap agent spend per session, and stop retrieval stuffing whole documents into prompts. For regulated workloads, a private small language model such as Dilr Mira removes the per-token meter entirely by running on your own hardware.

What is harness engineering?

Building the system around a model, the evals, tools, permissions, memory and observability, so an agent behaves reliably. The model is swappable; the harness is where the engineering lives. In March 2026 the LangChain team moved their coding agent from 30th to 5th on Terminal Bench 2.0 purely by re-engineering the harness, model unchanged. DILR builds it in DATS stage 04, governed by the Operating Model stage.

How much time do employees lose looking for information?

McKinsey puts it at roughly 1.8 hours a day, about 9.3 hours a week, spent searching for and gathering information: close to a quarter of the working day. A company knowledge base answers from your own documents with the source cited, so the answer is auditable and a wrong one traces to a file you can fix. Dilr Voice answers live calls from the same kind of knowledge base.

Which problem should a business tackle first?

The smallest honest start is the one with published pricing and no procurement: an AI receptionist from Dilr Voice at $0.14 a minute with $10 of trial credits, live the same day. For a larger organisation the right first move is a decision, not a product. The Placement Diagnostic takes four to six weeks and returns a ranked roadmap plus the list of things not to build. If you would rather read first, our approach and compliance changelog set out how we work.

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↗ Unfiltered

What Reddit says about these problems.

We are not going to paraphrase it for you. Reddit threads are not something we can cite the way we cite Faros or DORA above, so any tidy quote block here would be invented, and inventing sentiment on a page built out of named sources would defeat the point of the page. Here are the live searches instead. Read the threads and judge them yourself, and be suspicious of any vendor page that hands you conveniently supportive quotes.

Reddit: AI use casesReddit: AI developer productivityReddit: AI code review bottleneckReddit: Reducing LLM costsReddit: Agents in productionReddit: AI knowledge base / RAG

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Which AI solution is worth your quarter?

30 minutes, no deck. You leave with a view on which problem to solve first and what it is worth, whether or not you work with us.

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