Forecasta

BACKGROUND

Please describe your company and position.

I am the Founder of Faberiq.ai

Describe what your company does in a single sentence.

Faberiq.ai is an AI front office for small service businesses. It handles customer conversations across Instagram, WhatsApp, Facebook, SMS, email, and web chat, then qualifies leads, books appointments, sends quotes, and collects payments. Businesses connect their own tools, including Google and Microsoft calendars, Square, and Stripe, and can build custom automations in a visual workflow designer. Agents answer from the business's own documents through a knowledge base, so responses are grounded in what the business actually offers. We also serve agencies that white-label the platform for their clients, with a branded customer portal and embeddable widgets.

OPPORTUNITY / CHALLENGE

What specific goals or objectives did you hire Forecasta to accomplish?

• Make the platform's citation guarantee hold up in production across messy, real world documents such as scans, exports, tables, and nested clauses, not just clean text.
• Preserve verifiable provenance (document, page, exact position) through every stage of the pipeline, so every citation resolves to a real, checkable location.
• Build a calibrated "I don't know" path, so the agent says so instead of answering from general knowledge when retrieval finds nothing relevant.

SOLUTION

How did you find Forecasta?

Referral

Why did you select Forecasta over others?

• High ratings
• Great culture fit
• Referred to me
• Company values aligned

How many teammates from Forecasta were assigned to this project?

1 Employee

Describe the scope of work in detail. Please include a summary of key deliverables.

Forecasta owned one subsystem inside our platform: the pipeline between a customer uploading a document and our agent answering with a verifiable citation. They built it to our existing interfaces and worked alongside our internal team, who kept ownership of the workflow engine, agents, integrations, and everything else. The scope covered four stages. First, parsing documents in whatever format customers actually upload, including OCR for scans, and recording the source document, page, and position of every extracted piece of text. Second, chunking along each document's own structure, so tables stay intact and clauses stay whole instead of being cut at a fixed length. Third, hybrid search that combines keyword and semantic matching, so paraphrased questions find the right passage and exact terms like identifiers or numbers aren't missed. Fourth, returning answers with citations that resolve to the exact source location, and returning an explicit "insufficient grounding" response when nothing relevant is retrieved. The key deliverables were the production pipeline itself, the abstention behavior, an evaluation set and harness that measures citation accuracy so we can test any pipeline change against a number, and handover documentation for our internal team.

RESULTS & FEEDBACK

What were the measurable outcomes from the project that demonstrate progress or success?

Citation accuracy on a held out evaluation set (whether a cited span actually supports the claim) became measurable for the first time, and is now scored independently from answer quality. Add specific accuracy or unsupported answer rate figures here if available.

Describe their project management. Did they deliver items on time? How did they respond to your needs?

It was a fixed-scope engagement built against our interfaces, and communication went directly engineer to engineer the whole way through, with no account management layer in between. That kept decisions fast and meant questions got answered by the people actually writing the code. They were responsive to our requirements and stayed disciplined about scope, building their subsystem without drifting into the rest of our platform. 

When their first chunking approach started breaking tables and clause structure, they caught it themselves from the evaluation numbers and rebuilt that stage, about a week of work, rather than shipping something that scored fine on average but failed on the content that matters most. The parsing layer took longer than originally scoped because both sides underestimated how many document formats our customers upload, but they flagged it early and [the project still wrapped in April as planned

What was your primary form of communication with Forecasta?

• Virtual Meeting
• Email or Messaging App

What did you find most impressive or unique about this company?

They treated our citation requirement as the actual spec, not a nice-to-have. Plenty of teams can stand up a retrieval system that looks good in a demo. Forecasta took "every claim must point to a verifiable source" and let it drive every design decision, from how documents are parsed to how they're chunked. The clearest example was their chunking rebuild: average retrieval scores looked acceptable, but they dug into the evaluation results, saw citations failing specifically on tables and clauses, and fixed it even though nobody would have noticed from the headline numbers. That willingness to hold themselves to the hard requirement, backed by real measurement, is what set them apart.

Are there any areas for improvement or something Forecasta could have done differently?

Two things. First, the range of document formats our customers upload was underestimated at the start, on both sides, and the parsing layer took longer than scoped as a result. A short discovery phase sampling real customer files before committing to the plan would have caught that earlier. Second, handover documentation was mostly produced at the end. Writing it alongside the build would have let our internal team ramp up sooner and review decisions as they were made.

BACKGROUND

Introduce your business and what you do there.

I’m the CEO and co-founder of UtterVision, an automated platform that simplifies and streamlines visa and immigration applications for multiple countries. We offer step-by-step guidance, professional reviews, and a user-friendly experience without paperwork or consultation fees.

OPPORTUNITY / CHALLENGE

What challenge were you trying to address with Forecasta?

We needed help with AI automation development.

SOLUTION

What was the scope of their involvement?

Forecasta handled two engagements for us. The first was a two-week AI opportunity audit where they mapped our intake-to-filing process, evaluated the value and risk of automating each step, and created a prioritized automation roadmap.They did an excellent job mapping our entire intake-to-filing process and evaluating each step based on two key factors: the value automation could create and the regulatory or reputational risk it could introduce.They delivered a clear, sequenced roadmap that showed us what we should automate, what required additional safeguards, and, importantly, what we should not automate.One of the most valuable findings was that our eligibility criteria were scattered across spreadsheets, saved prompts, and internal knowledge, with no single up-to-date version or historical record. They identified this as a critical issue and explained why we couldn’t safely build automation on top of it until the criteria were properly centralized and structured.That audit led directly to the second engagement, which focused on building a versioned eligibility rule base and an internal assistant. The main deliverables were a centralized versioned ruleset, a test suite, the ability to replay historical eligibility decisions, an internal plain-language assistant, an editing and review process for our team, and the supporting documentation.They also identified another major opportunity that we hadn’t previously built: deadline and expiry tracking, including permit expiries, biometrics windows, and medical exam validity. This was ultimately identified as one of the highest-value, lowest-risk areas for automation.

What is the team composition?

We worked with one teammate from Forecasta.

How did you come to work with Forecasta?

We found them through an online search and a referral.

How much have you invested with them?

We spent less than $10,000.

What is the status of this engagement?

We worked together from July–August 2026.

RESULTS & FEEDBACK

What evidence can you share that demonstrates the impact of the engagement?

The clearest measurable outcome was that our ability to replay past eligibility decisions went from 0%–100%. Before Forecasta's work, if we needed to review a decision from eight months ago, we could only compare it against the current eligibility criteria, even though those criteria may have changed since then.After the project, we could reconstruct any past decision using the rules that were actually in force at that time, with the relevant rule version and supporting evidence. This gave us a much more reliable and auditable eligibility process.Overall, Forecasta gave us much more than an audit. They gave us a clear roadmap for what to build, what not to build, and how to approach automation responsibly. The work was extremely valuable and gave our team a much clearer direction.

How did Forecasta perform from a project management standpoint?

They delivered on time. We communicated via virtual meetings and email.

What did you find most impressive about them?

They had fast communication and a deep understanding of the problem.

Are there any areas they could improve?

No, there weren’t any.